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This article is part of the series Unstructured Information Management from Multimedia Data Sources.

Open Access Research Article

Discriminative Feature Selection via Multiclass Variable Memory Markov Model

Noam Slonim1*, Gill Bejerano2, Shai Fine3 and Naftali Tishby1

Author Affiliations

1 School of Engineering and Computer Science and Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem 91904, Israel

2 School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel

3 IBM Research Laboratory in Haifa, Haifa University, Mount Carmel, Haifa 31905, Israel

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EURASIP Journal on Advances in Signal Processing 2003, 2003:850172  doi:10.1155/S111086570321115X

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2003/2/850172

Received:18 April 2002
Revisions received:15 November 2002
Published:25 February 2003

© 2003 Copyright © 2003 Hindawi Publishing Corporation

We propose a novel feature selection method based on a variable memory Markov (VMM) model. The VMM was originally proposed as a generative model trying to preserve the original source statistics from training data. We extend this technique to simultaneously handle several sources, and further apply a new criterion to prune out nondiscriminative features out of the model. This results in a multiclass discriminative VMM (DVMM), which is highly efficient, scaling linearly with data size. Moreover, we suggest a natural scheme to sort the remaining features based on their discriminative power with respect to the sources at hand. We demonstrate the utility of our method for text and protein classification tasks.

variable memory Markov (VMM) model; feature selection; multiclass discriminative analysis

Research Article