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Interests:

  • Machine Learning and Data Mining
    • Graph Based Learning
    • Active Learning
    • Gaussian Process and Kernel Machines
    • Boosting and Ensembles
  • Pattern Recognition and Image Processing
  • Computer Vision
  • Bioinformation

Topics:

  • dimensionality reduction
  • distance metric learning
  • manifold learning
  • clustering
  • semi-supervised learning
  • SVM
  • kernel method
  • multiple kernel learning
  • active learning
  • feature selection
  • ranking
  • classifier ensemble
  • NMF
  • sparse method
  • multi-label learning
  • multi-task learning
  • multi-view learning
  • multi-instance learning
  • transfer learning
  • online learning
  • large scale learning
  • optimization
  • deep learning
  • dictionary learning
  • topic model
  • image segmentation
  • ...

Projects:

  • 2D barcode recognition system: 2005.5-2006.9
  • Handwritten digital recognition system: 2000.12 - 2001.6
Selected Publications (Full List and Codes):
  1. 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.
  2. Shiming Xiang, Feiping Nie, 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.
  3. Zhigang Ma, Yi Yang, Feiping Nie, Nicu Sebe, Shuicheng Yan, Alexander Hauptmann. Harnessing Lab Knowledge for Real-world Action Recognition.  International Journal of Computer Vision (IJCV), 109(1-2): 60-73, 2014.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Yangqing Jia, Feiping Nie, Changshui Zhang. Trace Ratio Problem RevisitedIEEE Transactions on Neural Networks (TNN), Volume 20, Issue 4, Pages 729-735, 2009.
  15. 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.
  16. 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.
  17. 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.  Bioinformatics28(18): i619-i625, 2012.
  18. 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.  Bioinformatics28(12): i127-i136, 2012.
  19. 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.  Bioinformatics28(2): 229-237, 2012.
  20. 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.
  21. Feiping Nie, Jianjun Yuan, Heng Huang. Optimal Mean Robust Principal Component Analysis. The 31st International Conference on Machine Learning (ICML), 2014.
  22. 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.
  23. Hua Wang, Feiping Nie, Heng Huang. Robust and Discriminative Self-Taught Learning. The 30th International Conference on Machine Learning (ICML), 2013.
  24. Hua Wang, Feiping Nie, Heng Huang. Multi-View Clustering and Feature Learning via Structured Sparsity. The 30th International Conference on Machine Learning (ICML), 2013.
  25. Deguang Kong, Chris Ding, Heng Huang, Feiping Nie. An Iterative Locally Linear Embedding Algorithm. The 29th International Conference on Machine Learning (ICML), 2012.
  26. Dijun Luo, Chris Ding, Feiping Nie, Heng Huang. Cauchy Graph Embedding.  The 28th International Conference on Machine Learning (ICML), Bellevue, 2011.
  27. 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%)
  28. 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.
  29. 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.
  30. 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%).
  31. 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.
  32. 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.
  33. 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.

 

 

 

 

 

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