Locality sensitive discriminant matrixized learning machine

Abstract

Differently from Vector-pattern-oriented Classifier Design (VecCD), Matrix-pattern-oriented Classifier Design (MatCD) is expected to manipulate matrix-oriented patterns directly rather than turning them into a vector, and further demonstrated its effectiveness. However, some prior information, such as the local sensitive discriminant information among matrix-oriented patterns, might be neglected by MatCD. To overcome such flaw, a new regularization term named RLSD is adopted into MatCD by taking advantage of Locality Sensitive Discriminant Analysis (LSDA) in this paper. In detail, the objective function of LSDA is modified and transformed into the regularization term RLSD to explore the local sensitive discriminant information among matrix-oriented patterns. In the implementation, RLSD is collaborated with one typical MatCD, whose name is Matrix-pattern-oriented Ho-Kashyap Classifier (MatMHKS), so as to create a new classifier based on local sensitive discriminant information named LSDMatMHKS for short. Finally, comprehensive experiments are designed to validate the effectiveness of LSDMatMHKS. The major contributions of this paper can be concluded as (1) improving the classification performance and the learning ability of MatCD, (2) introducing local sensitive discriminant information into MatCD and extending the application scenario of LSDA, and (3) validating and analyzing the feasibility and effectiveness of RLSD.