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  • Research Article
  • Open Access

Discriminative Feature Selection via Multiclass Variable Memory Markov Model

EURASIP Journal on Advances in Signal Processing20032003:850172

  • Received: 18 April 2002
  • Published:


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, Jerusalem, 91904, Israel
School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
IBM Research Laboratory in Haifa, Haifa University, Mount Carmel, Haifa, 31905, Israel


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