Communications inspired linear discriminant analysis

Chen, M

Carson, W

Rodrigues, M

Calderbank, R

Carin, L

2014-07-22T16:21:26Z

2012-10-10

We study the problem of supervised linear dimensionality reduction, taking an information-theoretic viewpoint. The linear projection matrix is designed by maximizing the mutual information between the projected signal and the class label. By harnessing a recent theoretical result on the gradient of mutual information, the above optimization problem can be solved directly using gradient descent, without requiring simplification of the objective function. Theoretical analysis and empirical comparison are made between the proposed method and two closely related methods, and comparisons are also made with a method in which Rényi entropy is used to define the mutual information (in this case the gradient may be computed simply, under a special parameter setting). Relative to these alternative approaches, the proposed method achieves promising results on real datasets. Copyright 2012 by the author(s)/owner(s).

https://hdl.handle.net/10161/8956

icml.cc / Omnipress

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Communications inspired linear discriminant analysis

Journal article

919

926

Computer Science

Duke

Electrical and Computer Engineering

Mathematics

Physics

Pratt School of Engineering

Trinity College of Arts & Sciences

Published

1