Elements of information theory
Elements of information theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Orthogonal series density estimation and the kernel eigenvalue problem
Neural Computation
Robust Active Shape Model Search
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Energy, entropy and information potential for neural computation
Energy, entropy and information potential for neural computation
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Face Recognition Using Laplacianfaces
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
Overview of the Face Recognition Grand Challenge
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
Feature Extraction Using Information-Theoretic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Equivalences between Kernel Methods and Information Theoretic Methods
Journal of VLSI Signal Processing Systems
Weighted and robust learning of subspace representations
Pattern Recognition
Fast principal component analysis using fixed-point algorithm
Pattern Recognition Letters
Principal component analysis for data containing outliers and missing elements
Computational Statistics & Data Analysis
Journal of Cognitive Neuroscience
KPCA for semantic object extraction in images
Pattern Recognition
Neurocomputing
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Bayesian functional PCA
Computational Statistics & Data Analysis
Robust PCA for skewed data and its outlier map
Computational Statistics & Data Analysis
Robust feature extraction via information theoretic learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
Robust Discriminant Analysis Based on Nonparametric Maximum Entropy
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Local smoothing for manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Kernel principal components are maximum entropy projections
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust Tensor Analysis With L1-Norm
IEEE Transactions on Circuits and Systems for Video Technology
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Robust principal component analysis by self-organizing rules based on statistical physics approach
IEEE Transactions on Neural Networks
Robust semi-supervised learning for biometrics
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
A regularized correntropy framework for robust pattern recognition
Neural Computation
Active shape model based on sparse representation
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Pattern Recognition Letters
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In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEnt-PCA, which is derived from a Parzen window estimation of Renyi's quadratic entropy. Instead of minimizing the reconstruction error either based on L"2-norm or L"1-norm, the MaxEnt-PCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEnt-PCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEnt-PCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods.