A novel approach to pattern recognition based on discriminative metric design

Abstract

This paper proposes a novel approach, named discriminative metric design (DMD), to pattern recognition. DMD optimizes the whole metrics of discriminant functions with the minimum classification error/generalized probabilistic descent method (MCE/GPD) such that the intrinsic features of each pattern class can be represented efficiently. The resulting metrics lead accordingly to robust recognizers. DMD is quite general. Several existing methods, such as learning vector quantization, subspace method, discriminative feature extraction, radial-basis function network, and the continuous hidden Markov model, are defined as its special cases. Among the many possibilities, this paper specifically elaborates the DMD formulation for recognizing fixed dimensional patterns using quadratic discriminant functions, and clearly demonstrates its utility in a speaker-independent Japanese vowel recognition task.