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2014
  • Feiping Nie, Yizhen Huang, Xiaoqian Wang, Heng Huang. New Primal SVM Solver with Linear Computational Cost for Big Data Classifications. The 31st International Conference on Machine Learning (ICML), 2014. CODE  A new linear time solver for SVM which can be easily implemented with only several lines of MATLAB code, and can be easily parallelized
  • Feiping Nie, Jianjun Yuan, Heng Huang. Optimal Mean Robust Principal Component Analysis. The 31st International Conference on Machine Learning (ICML), 2014. CODE  Consider the optimal mean in robust PCA, which is an easily ignored issue. Propose a very simple algorithm to solve a very general problem.
  • Hua Wang, Feiping Nie, Heng Huang. Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization. The 31st International Conference on Machine Learning (ICML), 2014.
  • Feiping Nie, Xiaoqian Wang, Heng Huang. Clustering and Projected Clustering with Adaptive Neighbors.  The 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), New York, USA, 2014. CODE  Solving a challenging and fundamental clustering problem with elegant optimization and with amazing performance!
  • De Wang*, Feiping Nie*, Heng Huang. Large-Scale Adaptive Semi-Supervised Learning via Unified Inductive and Transductive Model.  The 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), New York, USA, 2014.  CODE  Only one parameter r, which has multiple properties: 1. From macro view, r controls the weight of unlabeled data (larger r, less weight). 2. From micro view, given r, uncertain data lying around boundary have negligible weights (more uncertain, less weight).
2013
  • Chenping Hou, Feiping Nie, Dongyun Yi, Yi Wu. Efficient Image Classification via Multiple Rank Regression. IEEE Transactions on Image Processing (TIP), 22(1):340-352, 2013.  CODE  Propose a new 2D (or tensor) method, which trades off the learning ability and generalization. Traditional 2D method: weakest learning ability but best generalization. 1D method: best learning ability but weakest generalization.
  • Jin Huang, Feiping Nie, Heng Huang, Chris Ding. Robust Manifold Non-Negative Matrix Factorization. ACM Transactions on Knowledge Discovery from Data (TKDD), 8(3):11, 2013.  PDF CODE  Propose a new Laplacian regularized NMF method with non-trivial solution.
  • Jin Huang, Feiping Nie, Heng Huang, Yicheng Tu, Yu Lei. Social Trust Prediction Using Heterogeneous Networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(4):17, 2013.  CODE
  • Feiping Nie, Hua Wang, Heng Huang, Chris Ding. Joint Schatten p-Norm and lp-Norm Robust Matrix Completion for Missing Value Recovery. Knowledge and Information Systems (KAIS), to appear, 2013.  CODE
  • Yun Liu, Feiping Nie*, Jigang Wu, Lihui Chen. Efficient Semi-supervised Feature Selection with Noise Insensitive Trace Ratio Criterion. Neurocomputing, 105:12-18, 2013.  CODE  Analyze the noise influence to the trace ratio criterion for feature selection. Propose a simple and reasonable feature normalization approach for feature selection.
  • Chenping Hou, Feiping Nie, Changshui Zhang, Yi Wu. Learning a Subspace for Clustering via Pattern Shrinking. Information Processing & Management (IPM), 49(4):871-883, 2013.  CODE
  • Feiping Nie, Dong Xu, Ivor W. Tsang, Changshui Zhang. A Flexible and Effective Linearization Method for Subspace Learning. Book Chapter, 2013.   PDF  CODE
  • Hua Wang, Feiping Nie, Heng Huang. Robust and Discriminative Self-Taught Learning. The 30th International Conference on Machine Learning (ICML), 2013.
  • Hua Wang, Feiping Nie, Heng Huang. Multi-View Clustering and Feature Learning via Structured Sparsity. The 30th International Conference on Machine Learning (ICML), 2013.
  • Xiao Cai, Chris Ding, Feiping Nie, Heng Huang. On The Equivalent of Low-Rank Linear Regressions and Linear Discriminant Analysis Based Regressions. The 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013.  CODE
  • Jin Huang, Feiping Nie, Heng Huang, Yu Lei, Chris Ding. Social Trust Prediction Using Rank-k Matrix Recovery. The 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.  CODE
  • Feiping Nie, Hua Wang, Heng Huang, Chris Ding. Early Active Learning via Robust Representation and Structured Sparsity. The 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.  CODE
  • Feiping Nie, Hua Wang, Heng Huang, Chris Ding. Adaptive Loss Minimization for Semi-Supervised Elastic Embedding. The 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.  CODE
  • Xiao Cai, Feiping Nie, Heng Huang. Exact Top-k Feature Selection via l2,0-Norm Constraint. The 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.  CODE
  • Xiao Cai, Feiping Nie, Heng Huang. Multi-View K-Means Clustering on Big Data. The 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.  CODE
  • Zhigang Ma, Yi Yang, Feiping Nie, Sebe Nicu. Thinking of Images as What They Are: Compound Matrix Regression for Image Classification. The 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.  CODE
  • Jin Huang, Feiping Nie, Heng Huang, Chris Ding. Supervised and Projected Sparse Coding for Image Classification. The Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2013.  CODE
  • Jin Huang, Feiping Nie, Heng Huang. Robust Discrete Matrix Completion. The Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2013.  CODE
  • Jin Huang, Feiping Nie, Heng Huang. Spectral Rotation vs K-means in Spectral Clustering. The Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2013.  CODE  Theoretically understand why Spectral Rotation (SR) is better than K-means in the clustering with spectral relaxation.
  • Hua Wang, Feiping Nie, Heng Huang. Heterogeneous Visual Features Fusion via Sparse Multimodal Machine. The 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
  • Hua Wang, Feiping Nie, Weidong Cai, Heng Huang. Semi-Supervised Robust Dictionary Learning via Efficient l2,0+-Norms Minimization. International Conference on Computer Vision (ICCV), 2013. 
  • Xiao Cai, Feiping Nie, Weidong Cai, Heng Huang. New Graph Structured Sparsity Model for Multi-Label Image Annotations. International Conference on Computer Vision (ICCV), 2013.  CODE 
  • Xiao Cai, Feiping Nie, Weidong Cai, Heng Huang. Heterogeneous Image Features Integration via Multi-Modal Semi-Supervised Learning Model. International Conference on Computer Vision (ICCV), 2013.  CODE 
  • De Wang, Feiping Nie, Heng Huang, Jingwen Yan, Shannon Risacher, Andrew Saykin, Li Shen. Structural Brain Network Constrained Neuroimaging Marker Identification for Predicting Cognitive FunctionsThe International Conference on Information Processing in Medical Imaging (IPMI), 2013.  CODE
  • Heng Huang, Jingwen Yan, Feiping Nie, Jin Huang, Weidong Cai, Andrew J. Saykin, Li Shen. A New Sparse Learning Model for Brain Anatomical and Genetic Network Analysis. The 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nagoya, Japan, 2013. 
  • Guorong Wu, Feiping Nie, Qian Wang, Shu Liao, Daoqiang Zhang, and Dinggang Shen. Minimizing Joint Risk of Mislabeling for Iterative Patch-based Label Fusion. The 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nagoya, Japan, 2013. 
2012
  • Yi Yang, Feiping Nie, Dong Xu, Jiebo Luo, Yueting Zhuang, Yunhe Pan. A Multimedia Retrieval Framework based on Semi-Supervised Ranking and Relevance Feedback.  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 34(4):723-742, 2012.   CODE
  • Yi Yang, Fei Wu, Feiping Nie, Heng Tao Shen, Yueting Zhuang, Alexander G. Hauptmann. Web & Personal Image Annotation by Mining Label Correlation with Relaxed Visual Graph Embedding.  IEEE Transactions on Image Processing (TIP), 21(3):1339-1351, 2012.   CODE
  • Shiming Xiang, Feiping Nie, Gaofeng Meng, Chunhong Pan, Changshui Zhang. Discriminative Least Squares Regression for Multiclass Classification and Feature Selection.  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 42(1):17-27, 2012.  CODE  A novel and interesting formulation to make the least squares regression suitable for classification.
  • Yi Huang, Dong Xu, Feiping Nie. Semi-supervised Dimension Reduction using Trace Ratio Criterion.  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 23(3): 519-526, 2012.   CODE
  • Feiping Nie, Dong Xu, Xuelong Li. Initialization Independent Clustering with Actively Self-Training Method.  IEEE Transactions on Systems, Man and Cybernetics, Part B (TSMCB), 42(1):17-27, 2012.  CODE
  • Yi Huang, Dong Xu, Feiping Nie. Patch Distribution Compatible Semi-Supervised Dimension Reduction for Face and Human Gait Recognition.  IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 22(3):479-488, 2012.
  • Zhigang Ma, Feiping Nie, Yi Yang, Jasper Uijlings, Nicu Sebe, Alexander G. Hauptmann. Discriminating Joint Feature Analysis for Multimedia Content Understanding.  IEEE Transactions on Multimedia (TMM), 14(6):1662-1672, 2012.   CODE
  • Zhigang Ma, Feiping Nie, Yi Yang, Jasper Uijlings, Nicu Sebe. Web Image Annotation via Subspace-Sparsity Collaborated Feature Selection.  IEEE Transactions on Multimedia (TMM), 14(4):1021-1030.   CODE
  • Hua Wang, Feiping Nie, Heng Huang, Jingwen Yan, Sungeun Kim, Kwangsik Nho, Shannon L. Risacher, Andrew J. Saykin, Li Shen, ADNI. From Phenotype to Genotype: An Association Study of Candidate Phenotypic Markers to Alzheimer's Disease Relevant SNPs.  Bioinformatics, 28(18): i619-i625, 2012.  CODE
  • Hua Wang, Feiping Nie, Heng Huang, Shannon L. Risacher, Andrew J. Saykin, Li Shen, ADNI. Identifying Disease Sensitive and Quantitative Trait Relevant Biomarkers from Multi-Dimensional Heterogeneous Imaging Genetics Data via Sparse Multi-Modal Multi-Task Learning.  Bioinformatics, 28(12): i127-i136, 2012.   CODE
  • Hua Wang, Feiping Nie, Heng Huang, Sungeun Kim, Kwangsik Nho, Shannon Risacher, Andrew J Saykin, Li Shen, ADNI. Identifying Quantitative Trait Loci via Group-Sparse Multi-Task Regression and Feature Selection: An Imaging Genetics Study of the ADNI Cohort.  Bioinformatics, 28(2): 229-237, 2012.   CODE
  • Feiping Nie, Shiming Xiang, Yun Liu, Chenping Hou, Changshui Zhang. Orthogonal vs. Uncorrelated Least Squares Discriminant Analysis for Feature Extraction.  Pattern Recognition Letters (PRL), 33(5): 485-491, 2012.  CODE
  • Shizhun Yang, Chenping Hou, Feiping Nie, Yi Wu. Unsupervised maximum margin feature selection via L2,1-norm minimization. Neural Computing & Applications (NCA), 21(7):1791-1799, 2012.  CODE
  • Hua Wang, Feiping Nie, Heng Huang, Jingwen Yan, Sungeun Kim, Shannon Risacher, Andrew Saykin, Li Shen. High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer Disease Progression Prediction. The Twenty-Sixth Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA, 2012. Oral Paper.  (Acceptance Rate 20/1467=1.4%)
  • Dijun Luo, Chris Ding, Heng Huang, Feiping Nie. Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach. The Twenty-Sixth Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA, 2012.  CODE
  • Deguang Kong, Chris Ding, Heng Huang, Feiping Nie. An Iterative Locally Linear Embedding Algorithm. The 29th International Conference on Machine Learning (ICML), 2012.  CODE
  • Feiping Nie, Heng Huang, Chris Ding. Efficient Schatten-p Norm Minimization for Low-Rank Matrix Recovery. The 26th AAAI Conference on Artificial Intelligence (AAAI), Toronto, Ontario, Canada, 2012.   CODE
  • Hua Wang, Feiping Nie, Heng Huang. Robust and Discriminative Distance for Multi-Instance Learning. The 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, USA, 2012.
  • Hua Wang, Feiping Nie, Heng Huang, Chris Ding. Predicting Protein-Protein Interactions from Multimodal Biological Data Sources via Nonnegative Matrix Factorization. The 16th International Conference on Research in Computational Molecular Biology (RECOMB) , Barcelona, Spain, 2012. (acceptance rate <15.5%, 31/200+)
  • Hanbo Chen, Xiao Cai, Dajiang Zhu, Feiping Nie, Tianming Liu, Heng Huang. Group-wise Consistent Parcellation of Gyri via Adaptive Multi-view Spectral Clustering of Fiber Shapes. The 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, 2012.  CODE
  • Feiping Nie, Hua Wang, Xiao Cai, Heng Huang, Chris Ding. Robust Matrix Completion via Joint Schatten p-Norm and Lp-Norm Minimization. IEEE International Conference on Data Mining (ICDM), Brussels, Belgium, 2012.  CODE
  • Jin Huang, Feiping Nie, Heng Huang, Yicheng Tu. Trust Prediction via Aggregating Heterogeneous Social Networks.  The 21st ACM International Conference on Information and Knowledge Management (CIKM), 2012.  CODE
  • Taiyong Li, Jingwen Yan, Hua Wang, Feiping Nie, Heng Huang, Sungeun Kim, Shannon Risacher, Andrew Saykin, Li Shen. Joint estimation of AD status and cognitive performance using VBM summary statistics. 18th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Beijing, China, 2012.

2011

  • Feiping Nie, Zinan Zeng, Ivor Tsang, Dong Xu, Changshui Zhang. Spectral Embedded Clustering: A  Framework for In-Sample and Out-of-Sample Spectral Clustering.  IEEE Transactions on Neural Networks (TNN), 22(11):1796-1808, 2011.  CODE  A linearity regularization is proposed for spectral clustering in high dimensional case. Theoretical analysis shows that spectral clustering and its variants, K-means, discriminative K-means are all special cases of the new clustering method. The eigenvector associated with the eigenvalue 0 of the Laplacian matrix should not be discarded in spectral rotation. A conference version can be found in IJCAI'09.
  • Chenping Hou, Feiping Nie, Fei Wang, Changshui Zhang, Yi Wu. Semi-Supervised Learning Using Negative Labels.  IEEE Transactions on Neural Networks (TNN), 22(3):420-432, 2011.  CODE
  • Cheng Chen, Yueting Zhuang, Feiping Nie, Yi Yang, Fei Wu, Jun Xiao. Learning a 3D Human Pose Distance Metric from Geometric Pose Descriptor.  IEEE Transactions on Visualization and Computer Graphics (TVCG), 17(11):1676-1689, 2011.
  • Shiming Xiang, Feiping Nie, Chunhong Pan, Changshui Zhang. Regression Reformulations of LLE and LTSA with Locally Linear Transformation.  IEEE Transactions on Systems, Man and Cybernetics, Part B (TSMCB), 41(5):1250-1262, 2011.  Uncover the very close connection between LLE and LTSA, from which understand why LTSA is better for manifold learning.
  • Feiping Nie, Dong Xu, Xuelong Li, Shiming Xiang. Semi-Supervised Dimensionality Reduction and Classification through Virtual Label Regression.  IEEE Transactions on Systems, Man and Cybernetics, Part B (TSMCB), 41(3):675-685, 2011.   PDF  CODE
  • Shiming Xiang, Chunhong Pan, Feiping Nie, Changshui Zhang. Interactive Image Segmentation with Multiple Linear Reconstructions in Windows.  IEEE Transactions on Multimedia (TMM), 13(2):342-352, 2011.   PDF  CODE
  • Cheng Chen, Yi Yang, Feiping Nie, Jean-Marc Odobez. 3D Human Pose Recovery from Monocular Images via Efficient Visual Feature Selection.  Computer Vision and Image Understanding (CVIU), 115(3):290-299, 2011.
  • Hua Wang, Heng Huang, Farhad Kamangar, Feiping Nie, Chris Ding.  Maximum Margin Multi-Instance Learning.  The Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), Granada, Spain, 2011.
  • Dijun Luo, Chris Ding, Feiping Nie, Heng Huang. Cauchy Graph Embedding.  The 28th International Conference on Machine Learning (ICML), Bellevue, 2011.  (Acceptance Rate: 152/598=25.4%).  CODE
  • Hua Wang, Feiping Nie, Heng Huang, Fillia Makedon. Fast Nonnegative Matrix Tri-Factorization for Large-Scale Data Co-Clustering.  The 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, 2011. (Acceptance Rate:227/1325=17.1%).  CODE
  • Feiping Nie, Heng Huang, Chris Ding, Dijun Luo, Hua Wang. Robust Principal Component Analysis with Non-Greedy L1-Norm Maximization.  The 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, 2011. (Acceptance Rate:(227+173)/1325=30.2%).  PDF  CODE  A very simple algorithm to solve a general L1 norm maximization problem.
  • Chenping Hou, Feiping Nie, Dongyun Yi, Yi Wu. Feature selection via joint embedding learning and sparse regression.  The 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, 2011. (Acceptance Rate:(227+173)/1325=30.2%).  CODE
  • Hua Wang, Feiping Nie, Heng Huang. Learning Instance Specific Distance for Multi-Instance Classifications.  The 25th AAAI Conference on Artificial Intelligence (AAAI), San Francisco, 2011.  (Acceptance Rate: 242/975=24.8%).
  • Yi Yang, Heng Tao Shen, Feiping Nie, Rongrong Ji, Xiaofang Zhou. Nonnegative Spectral Clustering with Discriminative Regularization.  The 25th AAAI Conference on Artificial Intelligence (AAAI), San Francisco, 2011.  (Oral+Poster Acceptance Rate: 4.4%).   CODE
  • Xiao Cai, Feiping Nie, Heng Huang, Farhad Kamangar. Heterogeneous Image Features Integration via Multi-View Spectral Clustering.  The 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Bellevue, 2011.  CODE
  • Yang Yang, Yi Yang, Zi Huang, Heng Tao Shen, Feiping Nie. Tag Localization with Spatial Correlations and Joint Group Sparsity.  The 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Bellevue, 2011.  CODE
  • Feiping Nie, Hua Wang, Heng Huang, Chris Ding.  Unsupervised and Semi-supervised Learning via L1-norm Graph.  The 13th International Conference on Computer Vision (ICCV), Barcelona, Spain, 2011.  PDF  CODE  A novel and interesting formulation for the Laplacian embedding. Benefit from L1 norm minimization, most of the differences ||f_i-f_j|| are zero, such that the embedding has clear clustering structure.
  • Hua Wang, Feiping Nie, Heng Huang, Shannon L. Risacher, Andrew J. Saykin, Li Shen, ADNI.  Sparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance.   The 13th International Conference on Computer Vision (ICCV), Barcelona, Spain, 2011.  CODE
  • Hua Wang, Feiping Nie, Heng Huang.  Transfer Dyadic Knowledge for Cross-Domain Image Classification.   The 13th International Conference on Computer Vision (ICCV), Barcelona, Spain, 2011.
  • Hua Wang, Heng Huang, Feiping Nie, Chris Ding.  Cross-Language Web Page Classification via Dual Knowledge Transfer Using Nonnegative Matrix Tri-Factorization.  The 34th Annual ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Beijing, 2011.  (Regular paper, 108/545=19.8%)
  • Zhigang Ma, Yi Yang, Feiping Nie, Jasper Uijlings, Nicu Sebe.  Exploiting the Entire Feature Space with Sparsity for Automatic Image Annotation.  ACM International Conference on Multimedia (ACM MM), Scottsdale, USA, 2011. (Full paper, 17%)   CODE
  • Dijun Luo, Chris Ding, Heng Huang, Feiping Nie. Consensus Spectral Clustering in Near-Linear Time.  The IEEE International Conference on Data Engineering (ICDE), Hannover, 2011.  (Acceptance Rate: 98/494=19.8%).  CODE
  • Hua Wang, Feiping Nie, Heng Huang, Shannon Risacher, Andrew J Saykin, Li Shen, ADNI. Identifying AD-Sensitive and Cognition-Relevant Imaging Biomarkers via Joint Classification and Regression.  The 14th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Toronto, 2011.  (Oral paper. Acceptance Rate: 34/819=4.2%).  CODE
  • Dijun Luo, Feiping Nie, Chris Ding, Heng Huang. Multi-Subspace Discovery and Representation.  The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens, Greece, 2011.  (Acceptance Rate: 121/599=20.2%).   CODE   Best Student Paper Runner-up Award
  • Hua Wang, Feiping Nie, Heng Huang, and Chris Ding.  Nonnegative Matrix Tri-Factorization Based High-Order Co-Clustering and Its Fast Implementation.  IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, 2011. (Full paper, 101/806=12.5%)
  • Xiao Cai, Feiping Nie, Heng Huang, and Chris Ding.  Multi-Class L2,1-Norms Support Vector Machine.  IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, 2011.  (Full paper, 101/806=12.5%)  CODE  A convex feature selection method based on multi-class SVM.
  • Hua Wang, Feiping Nie, Heng Huang, Yi Yang.  Learning frame relevance for video classification.  ACM International Conference on Multimedia (ACM MM), Scottsdale, USA, 2011.
  • Chenping Hou, Feiping Nie, Yi Wu.  Semi-supervised Dimensionality Reduction via Harmonic Functions.  The 8th International Conference on Modeling Decisions for Artificial Intelligence (MDAI), 2011.

2010

  • Shiming Xiang, Feiping Nie, and Changshui Zhang. Semi-Supervised Classification via Local Spline Regression.  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 32, no. 11, pp.2039-2053, 2010.  PDF  CODE
  • Feiping Nie, Dong Xu, Ivor W. Tsang, Changshui Zhang. Flexible Manifold Embedding: A Framework for Semi-supervised and Unsupervised Dimension Reduction.  IEEE Transactions on Image Processing (TIP), 19(7):1921-1932, 2010.  PDF  CODE  A general linearization framework for dimensionality reduction from which new method could achieve improved performance. Interestingly, two well studied linearizations (LPP like and SR like) are two extreme cases of the framework (ur->inf and ur->0).
  • Yi Yang, Dong Xu, Feiping Nie, Shuicheng Yan, Yueting Zhuang. Image Clustering using Local Discriminant Models and Global Integration.  IEEE Transactions on Image Processing (TIP), 19(10):2761-2773, 2010.  PDF  CODE
  • Shiming Xiang, Chunhong Pan, Feiping Nie, and Changshui Zhang. TurboPixel Segmentation Using  Eigen-Images.  IEEE Transactions on Image Processing (TIP). 19(11):3024-3034, 2010.  CODE
  • Dijun Luo, Heng Huang, Chris Ding, Feiping Nie. On The Eigenvectors of p-Laplacian.  Machine Learning (ML), 81(1):37-51, 2010.  CODE
  • Chenping Hou, Changshui Zhang, Yi Wu, Feiping Nie. Multiple View Semi-Supervised Dimensionality Reduction. Pattern Recognition (PR), Volume 43, Issue 3, Pages 720-730, 2010.  CODE
  • Changshui Zhang, Feiping Nie*, Shiming Xiang. A General Kernelization Framework for Learning Algorithms Based on Kernel PCANeurocomputing, 2010, 73(4-6): 959-967.   PDF  CODE  Almost all the linear methods could be easily kernelized via performing the linear method with transformed data by KPCA. Benefiting from this general kernelization framework, it is not necessary to derive the kernel version for each specific linear method.
  • Fei Wu, Wenhua Wang, Yi Yang, Yueting Zhuang, Feiping Nie. Classification by Semi-supervised Discriminative Regularization. Neurocomputing, 73(10-12):1641--1651, 2010.  CODE
  • Feiping Nie, Shiming Xiang, Yun Liu, Changshui Zhang. A General Graph-Based Semi-Supervised Learning with Novel Class DiscoveryNeural Computing Applications (NCA), 2010, 19(4): 549-555.  PDF  CODE  A general label propagation is proposed, and analyze how to make it has the following abilities: remove label noise, discover new class, perform ranking (learn with only positive labels).
  • Feiping Nie, Heng Huang, Xiao Cai, Chris Ding. Efficient and Robust Feature Selection via Joint L21-Norms Minimization.  Advances in Neural Information Processing Systems 23 (NIPS), 2010.  (Acceptance Rate: 293/1219=24.0%).  PDF  POSTER  CODE  DATA  The proposed reweighted method is very easy to implement, and can be used to solve very general problem. For example, it can be used to minimize almost all the existing robust loss functions!
  • Yi Yang, Feiping Nie, Shiming Xiang, Yueting Zhuang, Wenhua Wang. Local and Global Regressive Mapping for Manifold Learning with Out-of-Sample Extrapolation.  The 24rd AAAI Conference on Artificial Intelligence (AAAI), Atlanta, 2010.  (Oral paper, Acceptance Rate: 264/982=26.9%).   PDF  CODE
  • Feiping Nie, Chris Ding, Dijun Luo, Heng Huang. Improved MinMax Cut Graph Clustering with Nonnegative Relaxation.  The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Barcelona, 2010.  (Oral paper, Acceptance Rate: 120/658=18.2%).  PDF  CODE
  • Yi Huang, Dong Xu, Feiping Nie. Regularized Trace Ratio Discriminant Analysis with Patch Distribution Feature for Human Gait Recognition.  The International Conference on Image Processing (ICIP), Hongkong, 2010.  (Oral paper, Acceptance Rate: 1190/2500=47.6%).  CODE
  • Yun Liu, Feiping Nie, Jigang Wu, Lihui Chen. Semi-supervised Feature Selection Based on Label Propagation and Subset Selection. The International Conference on Computer and Information Application (ICCIA), Tianjin, 2010.  (Oral paper).  CODE

2009

  • Shiming Xiang, Feiping Nie, Chunxia Zhang and Changshui Zhang. Interactive Natural Image Segmentation via Spline RegressionIEEE Transactions on Image Processing (TIP), Volume 18, Issue 7, Pages1623-1632, 2009.  PDF  CODE
  • Yangqing Jia, Feiping Nie, Changshui Zhang. Trace Ratio Problem RevisitedIEEE Transactions on Neural Networks (TNN), Volume 20, Issue 4, Pages 729-735, 2009. PDF  CODE  Trace ratio is an important criterion in dimensionality reduction and multi-objective optimization since it is parameter free. A fast algorithm is proposed and theoretical anlysis show its convergence rate is quadratic.
  • Shiming Xiang, Feiping Nie, Changshui Zhang and Chunxia Zhang. Nonlinear Dimensionality Reduction with Local Spline EmbeddingIEEE Transactions on Knowledge and Data Engineering(TKDE), Volume 21, Issue 9, Pages 1285-1298, 2009.  PAGE  PDF  CODE  DATA 
  • Feiping Nie, Shiming Xiang, Yangqing Jia, Changshui Zhang. Semi-Supervised Orthogonal Discriminant Analysis via Label PropagationPattern Recognition (PR), Volume 42, Issue 11, Pages 2615-2627, 2009. PDF  CODE
  • Feiping Nie, Shiming Xiang, Yangqiu Song, Changshui Zhang. Extracting the Optimal Dimensionality for Local Tensor Discriminant AnalysisPattern Recognition (PR), Volume 42, Issue 1, Pages 105-114, 2009. PDF  CODE
  • Shiming Xiang, Feiping Nie, Yangqiu Song, Changshui Zhang and Chunxia Zhang, Embedding New Data Points for Manifold Learning via Coordinate PropagationKnowledge and Information Systems (KAIS) journal, Volume 19, Issue 2, Pages 159-184, 2009.   CODE
  • Chunxia Zhang, Shiming Xiang, Feiping Nie, Yangqiu Song: Nonlinear Dimensionality Reduction with Relative Distance ComparisonNeurocomputing, Volume 72, Issue 7-9, Pages 1719-1731, 2009.   CODE
  • Chenping Hou, Feiping Nie, Changshui Zhang and Yi Wu. Learning an Orthogonal And Smooth Subspace for Image ClassificationIEEE Signal Processing Letters (SPL), 16(4):303-306, 2009.  CODE
  • Changshui Zhang, Feiping Nie, Shiming Xiang, Chenping Hou. Soft Constraint Harmonic Energy Minimization for Transductive Learning and Its Two InterpretationsNeural Processing Letters (NPL), 30(2):89-102, 2009.
  • Chenping Hou, Feiping Nie, Changshui Zhang, Yi Wu. Learning a Subspace for Face Image Clustering via Trace Ratio CriterionOptical Engineering (OE), 48(6), 2009.  CODE
  • Feiping Nie, Shiming Xiang, Yangqiu Song and Changshui Zhang. Orthogonal Locality Minimizing Globality Maximizing Projections for Feature ExtractionOptical Engineering (OE),  Volume 18, Issue 1,  2009.  PDF  CODE  In LPP, the X*D*X' should be X*H_D*X', otherwise the data MUST be centerized before using LPP. The reason why X*D*X' was derived is analyized, and how to derive the correct X*H_D*X' is provided.
  • Feiping Nie, Dong Xu, Ivor W.Tsang, Changshui Zhang. Spectral Embedded ClusteringThe 21st International Joint Conference on Artificial Intelligence (IJCAI), Pasadena, USA, 2009.  (Oral paper, Acceptance Rate: 25.7%).  PDF  CODE  SLIDE
  • Yi Yang, Dong Xu, Feiping Nie, Jiebo Luo and Yueting Zhuang. Ranking with Local Regression and Global Alignment for Cross Media Retrieval. ACM International Conference on Multimedia (ACM MM), Beijing, China, 2009. (Full paper, Acceptance Rate: 16%).  PDF  CODE  SLIDE
  • Mingjie Qian, Feiping Nie, Changshui Zhang. Efficient Multi-class Unlabeled Constrained Semi-supervised SVMThe 18th ACM Conference on Information and Knowledge Management (CIKM), 2009.  CODE
  • Mingjie Qian, Feiping Nie, Changshui Zhang. Probabilistic labeled Semi-supervised SVM. Workshop on Optimization Based Methods for Emerging Data Mining Problems in conjunction with IEEE International Conference on Data Mining (ICDM Workshop), Florida, 2009.   CODE

2008

  • Shiming Xiang, Feiping Nie, and Changshui Zhang. Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition (PR), Volume 41, Issue 12, Pages 3600 - 3612, 2008.  PDF  CODE
  • Shiming Xiang, Feiping Nie, Yangqiu Song, and Changshui Zhang. Contour graph based human tracking and action sequence recognition. Pattern Recognition (PR), Volume 41, Issue 12, Pages 3653 - 3664, 2008.  PDF
  • Yangqiu Song, Feiping Nie, Changshui Zhang, Shiming Xiang. A Unified Framework for Semi-Supervised Dimensionality Reduction. Pattern Recognition (PR), Volume 41, Issue 9, Pages 2789-2799, 2008.  PDF  Independently proposed the Semi-superivsed Discriminant Analysis (SDA) and its many variants.
  • Yangqiu Song, Feiping Nie, Changshui Zhang. Semi-Supervised Sub-Manifold Discriminant Analysis.  Pattern Recognition Letters (PRL), 2008. To appear.  PDF
  • Feiping Nie, Shiming Xiang, Yangqing Jia, Changshui  Zhang, Shuicheng Yan. Trace Ratio Criterion for Feature Selection. The Twenty-Third AAAI Conference on Artificial Intelligence (AAAI), Chicago, 2008. oral paper.  PDF  CODE  SLIDE  An efficient algorithm to find the globally optimal solution to the subset selection problem with trace ratio criterion.

2007

  • Yangqiu Song, Qutang Cai, Feiping Nie and Changshui Zhang. Semi-Supervised Additive Logistic Regression: A Gradient Descent SolutionTsinghua Science and Technology (TST),12(6), pp.638-646, 2007. 
  • Feiping Nie, Shiming Xiang, Changshui Zhang. Neighborhood MinMax ProjectionsThe Twentieth International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, 2007.  (Oral paper, Acceptance Rate: 15.8%).  PDF  CODE  An idea of local scatter matrices is proposed for discriminant analysis. The trace ratio problem is studied and show an iteresting property: larger dimension, smaller optimal objective value.
  • Shiming Xiang, Feiping Nie, Yangqiu Song, Changshui Zhang, and Chunxia Zhang. Embedding New Data Points for Manifold Learning via Coordinate Propagation. 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2007. (Oral paper, Acceptance Rate: 4.67%).  CODE   Best paper award honorable mention 
  • Feiping Nie, Shiming Xiang, Yangqiu Song, Changshui Zhang. Optimal Dimensionality Discriminant Analysis and Its Application to Image Recognition1st Workshop on Component Analysis Methods for Classification, Clustering, Modeling and Estimation Problems in Computer Vision (CVPR workshop). 2007.  PDF  CODE
  • Feiping Nie, Shiming Xiang, Yangqiu Song, Changshui Zhang.  Extracting the Optimal Dimensionality for Discriminant Analysis. The 32nd International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hawaii, USA, 2007.   PDF  CODE
  • Shiming Xiang, Feiping Nie, Changshui Zhang, Chunxia Zhang. Interactive Visual Object Exaction Based on Belief Propagation. The 13th International Conference on Multimedia Modeling (MMM), Singapore, 2007. oral paper.

2006

  • Shiming Xiang, Feiping Nie, Changshui Zhang and Chunxia Zhang. Spline Embedding for Nonlinear Dimensionality Reduction. European Conference on Machine Learning (ECML), Berlin, Germany, 2006. LNAI 4212, pp. 825-832.  CODE
  • Shiming Xiang, Feiping Nie, and Changshui Zhang. Contour Matching Based on Belief Propagation. In: Lecture Notes in Computer Science, Poceedings, Springer-Verlag GmbH, ISBN, vol. 3851: pp. 489-498. Asian Conference on Computer Vision (ACCV), Hyderabad, India, January 13-16, 2006.  oral paper
  • Shiming Xiang, Feiping Nie, and Changshui Zhang. Exemplar-Based Human Contour Tracking. In: Lecture Notes in Computer Science, Poceedings, Springer-Verlag GmbH, ISBN, vol. 3851: pp. 338-347. Asian Conference on Computer Vision (ACCV), Hyderabad, India, January 13-16, 2006.
  • Shiming Xiang, Feiping Nie, and Changshui Zhang. Texture Image Segmentation: An Interactive Framework Based on Adaptive Features and Transductive Learning. In:  Lecture Notes in Computer Science, Poceedings, Springer-Verlag GmbH, ISBN, vol. 3851: pp. 216-225. Asian Conference on Computer Vision (ACCV), Hyderabad, India, January 13-16, 2006.

* indicates corresponding author or co-first author