In this letter, we present a novel exponentially embedded families (EEF)
based classification method, in which the probability density function (PDF) on
raw data is estimated from the PDF on features. With the PDF construction, we
show that class-specific features can be used in the proposed classification
method, instead of a common feature subset for all classes as used in
conventional approaches. We apply the proposed EEF classifier for text
categorization as a case study and derive an optimal Bayesian classification
rule with class-specific feature selection based on the Information Gain (IG)
score. The promising performance on real-life data sets demonstrates the
effectiveness of the proposed approach and indicates its wide potential
applications.Comment: 9 pages, 3 figures, to be published in IEEE Signal Processing Letter.
IEEE Signal Processing Letter, 201
