Discriminative metric design for pattern recognition

This paper proposes a new approach, named discriminative metric design (DMD), to pattern recognition. DMD optimizes discriminant functions with the minimum classification error/generalized probabilistic descent method (MCE/GPD) such that intrinsic features of each pattern class can be represented efficiently. Resulting metrics accordingly lead to robust recognizers. The DMD is quite general. Several existing methods, such as learning vector quantization and the continuous hidden Markov model, are defined as its special cases. The paper specially elaborates the DMD formulation for the quadratic discriminant function, and clearly demonstrates its utility in a speaker-independent Japanese vowel recognition task