Differently from Vector-pattern-orientedclassifier Design (VecCD), matrix-pattern-oriented classifier Design (MatCD) is expected to manipulate matrix-orientedpatterns directly rather than turning them into a vector,...
详细信息
Differently from Vector-pattern-orientedclassifier Design (VecCD), matrix-pattern-oriented classifier Design (MatCD) is expected to manipulate matrix-orientedpatterns 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-orientedpatterns, might be neglected by MatCD. To overcome such flaw, a new regularization term named Rim 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 RED to explore the local sensitive discriminant information among matrix-orientedpatterns. In the implementation, R-LSD 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 R-LSD). (C) 2016 Elsevier B.V. All rights reserved.
暂无评论