Solution of the concave linear complementarity problem
Recent advances in global optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
ICML '06 Proceedings of the 23rd international conference on Machine learning
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Outlier-resisting graph embedding
Neurocomputing
Generalized Power Method for Sparse Principal Component Analysis
The Journal of Machine Learning Research
Convex approximations to sparse PCA via Lagrangian duality
Operations Research Letters
Robust Tensor Analysis With L1-Norm
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose Lp-norm generalized principal component analysis (PCA) by maximizing a class of convex objective functions. The successive linearization technique is used to solve the proposed optimization model. It is interesting to note that the closed-form solution of the subproblem in the algorithm can be achieved at each iteration. Meanwhile, we theoretically prove the convergence of the proposed method under proper conditions. It is observed that sparse or non-sparse projection vectors can be obtained due to the applications of the Lp norm. In addition, one deflation scheme is also utilized to obtain many projection vectors. Finally, a series of experiments on face images and UCI data sets are carried out to demonstrate the effectiveness of the proposed method.