Title: Feature Selection using One Class SVM: A New Perspective
Speaker: Yamuna Prasad
Abstract:
Feature selection is an important task for mining useful information from datasets
in high dimensions, a typical characteristic of biology domains such as microar-
ray datasets. In this paper, we present an altogether new perspective on feature
selection. We pose feature selection as a one class SVM problem of modeling
the space in which features can be represented. We show that finding the support
vectors in our one class formulation is tantamount to performing feature selection.
Further, we show that our formulation reduces to the standard QPFS formulation
in the dual problem space. Not only our formulation gives new insights into the
task of feature selection, solving it directly in the primal space can give significant
computational gains when the number of the samples is much smaller than the
number of features. We validate our thesis by experimenting on three different
microarray datasets.