General Averaged Divergence Analysis

Authors:
Dacheng Tao;Xuelong Li;Xindong Wu;Stephen J. Maybank
Affiliations:
-;-;-;-
Venue:
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Year:
2007

Citing 0
Cited 21

Dimensionality reduction for heterogeneous dataset in rushes editing

Pattern Recognition
Feature extraction using Radon and wavelet transforms with application to face recognition

Neurocomputing
Binary sparse nonnegative matrix factorization

IEEE Transactions on Circuits and Systems for Video Technology
A new and fast implementation for null space based linear discriminant analysis

Pattern Recognition
Generalized discriminant analysis: a matrix exponential approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Photo-sketch synthesis and recognition based on subspace learning

Neurocomputing
Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes

Pattern Recognition
Metric learning with feature decomposition for image categorization

Neurocomputing
Incremental learning of LDA model for Chinese writer adaptation

Neurocomputing
Pose invariant virtual classifiers from single training image using novel hybrid-eigenfaces

Neurocomputing
Discrminative geometry preserving projections

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Evolutionary cross-domain discriminative hessian eigenmaps

IEEE Transactions on Image Processing
Biologically inspired feature manifold for scene classification

IEEE Transactions on Image Processing
Incremental embedding and learning in the local discriminant subspace with application to face recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sparse two-dimensional local discriminant projections for feature extraction

Neurocomputing
Face Recognition Using Kernel UDP

Neural Processing Letters
A remarkable standard for estimating the performance of 3D facial expression features

Neurocomputing
Multi-scale gist feature manifold for building recognition

Neurocomputing
Generalized mean for feature extraction in one-class classification problems

Pattern Recognition
An effective unconstrained correlation filter and its kernelization for face recognition

Neurocomputing
Sparse regularization discriminant analysis for face recognition

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Subspace selection is a powerful tool in data mining. An important subspace method is the Fisher Rao linear discriminant analysis (LDA), which has been successfully applied in many fields such as biometrics, bioinformatics, and multimedia retrieval. However, LDA has a critical drawback: the projection to a subspace tends to merge those classes that are close together in the original feature space. If the separated classes are sampled from Gaussian distributions, all with identical covariance matrices, then LDA maximizes the mean value of the Kullback Leibler (KL) divergences between the different classes. We generalize this point of view to obtain a framework for choosing a subspace by 1) generalizing the KL divergence to the Bregman divergence and 2) generalizing the arithmetic mean to a general mean. The framework is named the general averaged divergence analysis (GADA). Under this GADA framework, a geometric mean divergence analysis (GMDA) method based on the geometric mean is studied. A large number of experiments based on synthetic data show that our method significantly outperforms LDA and several representative LDA extensions.