Open Access

Discriminative Feature Selection via Multiclass Variable Memory Markov Model

EURASIP Journal on Advances in Signal Processing20032003:850172

Received: 18 April 2002

Published: 25 February 2003


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

Authors’ Affiliations

School of Engineering and Computer Science and Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem
School of Engineering and Computer Science, The Hebrew University of Jerusalem
IBM Research Laboratory in Haifa, Haifa University


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