Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Matrix computations (3rd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
The Journal of Machine Learning Research
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generalized spectral bounds for sparse LDA
ICML '06 Proceedings of the 23rd international conference on Machine learning
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Spectral Regression: A Unified Approach for Sparse Subspace Learning
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Convex multi-task feature learning
Machine Learning
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Trace ratio criterion for feature selection
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Towards Structural Sparsity: An Explicit l2/l0 Approach
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Exploiting fisher and fukunaga-koontz transforms in chernoff dimensionality reduction
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Fisher criterion has achieved great success in dimensionality reduction. Two representative methods based on Fisher criterion are Fisher Score and Linear Discriminant Analysis (LDA). The former is developed for feature selection while the latter is designed for subspace learning. In the past decade, these two approaches are often studied independently. In this paper, based on the observation that Fisher score and LDA are complementary, we propose to integrate Fisher score and LDA in a unified framework, namely Linear Discriminant Dimensionality Reduction (LDDR). We aim at finding a subset of features, based on which the learnt linear transformation via LDA maximizes the Fisher criterion. LDDR inherits the advantages of Fisher score and LDA and is able to do feature selection and subspace learning simultaneously. Both Fisher score and LDA can be seen as the special cases of the proposed method. The resultant optimization problem is a mixed integer programming, which is difficult to solve. It is relaxed into a L2,1-norm constrained least square problem and solved by accelerated proximal gradient descent algorithm. Experiments on benchmark face recognition data sets illustrate that the proposed method outperforms the state of the art methods arguably.