Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Estimation of entropy and mutual information
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
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Linear Feature Extractors Based on Mutual Information
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
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
Correntropy based feature selection using binary projection
Pattern Recognition
A regularized correntropy framework for robust pattern recognition
Neural Computation
Neurocomputing
Robust spectral regression for face recognition
Neurocomputing
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Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn features that account for both types of information based on variational optimization of nonparametric learning criteria. Using mutual information and Bayes error rate as example criteria, we show that high-quality features can be learned from a variational graph embedding procedure, which is solved through an iterative EM-style algorithm where the E-Step learns a variational affinity graph and the M-Step in turn embeds this graph by spectral analysis. The resulting feature learner has several appealing properties such as maximum discrimination , maximum-relevance- minimum-redundancy and locality-preserving . Experiments on benchmark face recognition data sets confirm the effectiveness of our proposed algorithms.