Sparse PCA (18 papers)
H. Zou, T. Hastie, R. Tibshirani, "Sparse principal component analysis", Journal of Computational and Graphical Statistics, Volume 15, pages 262-266, 2006.
I. Johnstone, A. Lu, "On Consistency and Sparsity for Principal Components Analysis in High Dimensions", Journal of the American Statistical Association, pages 682–693, 2009.
W. Liu, H. Zhang, D. Tao, Y. Wang, K. Lu, "Large-scale paralleled sparse principal component analysis", Multimedia Tools and Applications, pages 1-13, 2014.
M. Hubert, T. Reynkens, E. Schmit, "Sparse PCA for high-dimensional data with outliers", Technometrics, 2016.
S. Yi, Z. Lai, Z. He, Y. Cheung, Y. Liu, "Joint sparse principal component analysis" ,Pattern Recognition, Volume 61, pages 524-536, 2017.
Z. Hu, G. Pan, Y. Wang, Z. Wu, “Sparse Principal Component Analysis via Rotation and Truncation”, Chapter in Advances in Principal Component Analysis, pages 1-18, 2018.
A. Prasadan, R. Nadakuditi, D. Paul, "Sparse Equisigned PCA: Algorithms and Performance Bounds in the Noisy Rank-1 Setting.", Electronic Journal of Statistics. Volume 14, pages 345-385, 2020.
J. Camacho, R. Theron, J. García-Gimenez, G. Macia-Fernandez, P. García-Teodoro, “Group-Wise Principal Component Analysis for Exploratory Intrusion Detection”, IEEE Access, 2019.
J. Camacho, R. Rodriguez-Gomez, E. Saccenti, “Group-wise Principal Component Analysis for Exploratory Data Analysis”, Journal of Computational and Graphical Statistics, Volume 26, No. 3, pages 501-512, 2017.
J. Camacho, A. Smilde, “All sparse PCA models are wrong, but some are useful”, Scandinavian Symposium on Chemometrics, SSC 2019, Oslo, Norway, 2019.
A. Seghouane, N. Shokouhi, I. Koch, “Sparse principal component analysis with preserved sparsity pattern", IEEE Transactions on Image Processing, Volume 28, pages 3274–3285, 2019.
A. Prasadan, “Learning, Inference, and Unmixing of Weak, Structured Signals in Noise”, PhD Thesis, University of Michigan, 2020.
A. Prasadan, R. Nadakuditi “Sparse equisigned PCA: Algorithms and performance bounds in the noisy rank-1 setting”, Electronic Journal of Statistics, Volume 14, pages 345-385, 2020.
F. Chen, F. Rohe, "A New Basis for Sparse PCA", Preprint, 2020.
X. Liu, T. Yan, K. Wang, "Manifold Proximal Gradient with Momentum for Sparse PCA," IEEE International Conference on Image Processing and Media Computing, ICIPMC 2023, Xi'an, China, pages 155-158, 2023.
Z. Li, F. Nie, J. Bian, D. Wu, X. Li, "Sparse PCA via l2,p-Norm Regularization for Unsupervised Feature Selection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 45, No. 4, pages 5322-5328, 2023.
F. Nie, Q. Chen, W. Yu, X. Li, "Row-Sparse Principal Component Analysis via Coordinate Descent Method”, IEEE Transactions on Knowledge and Data Engineering, 2024.
S. Kumar, P. Sarkar, “Thresholded Oja does Sparse PCA?”,¨Preprint, February 2024.
Robust Sparse PCA (3 papers)
W. Ling, J. Yin, "The Robust Sparse PCA for Data Reconstructive via Weighted Elastic Net", International Conference on Communications, Signal Processing, and Systems, pages 225-234, 2012.
Q. Zhao, D. Meng, Z. Xu, "Robust Sparse Principal Component Analysis", Information Sciences, Volume 57, pages 1-14, September 2014.
M. Razzak, R. Saris, M. Blumenstein, G. Xu, "Robust 2D Joint Sparse Principal Component Analysis with F-norm Minimization for Sparse Modelling: 2D-RJSPCA", IEEE International Joint Conference on Neural Networks, IJCNN 2018, July 2018.