Enhanced multi-weight vector projection support vector machine

doi:10.1016/j.patrec.2014.02.006

Return

Enhanced multi-weight vector projection support vector machine

2014-06-01

PRE

Qiaolin Ye *

Ning Ye

Tongming Yin

DOI：10.1016/j.patrec.2014.02.006

Original

Original request for help

Save

Abstract

En 中文

Recently, we have developed an effective classifier, called Multi-weight vector projection support vector machine (MVSVM). Like traditional multisurface support vector machine Generalized-Eigenvalue-based Mulitisurface Support Vector Machine (GEPSVM), MVSVM can fast complete the computation and simultaneously handle the complex Exclusive Or (XOR) problems well. In addition, MVSVM still shows the more promising results than GEPSVM for different classification tasks. Despite the effectiveness of MVSVM, there is a serious limitation, which is that the number of the projection weight vectors for each class is limited to one. Intuitively, it is not enough to use only one projection weight vector for each class to obtain better classification. In order to address this problem, we, in this paper, develop enhanced MVSVM (EMVSVM), which is based on MVSVM. For a particular class, EMVSVM maximizes the distances from its projected average vector to the projected points from different classes to find better separability, which is different from MVSVM which maximizes the separability between classes by enforcing the maximization of the distances between the average vectors of different classes. Doing so can make EMVSVM obtain more than one discriminative weight-vector projections for each class due to that the rank of the newly-formed between-class scatter matrix is enlarged. From the statistical viewpoint, we analyze the proposed approach. Experimental results on public datasets indicate the effectiveness and efficiency of EMVSVM. (C) 2014 Elsevier B.V. All rights reserved.

Keywords:

Multiple weight vectors

Classification

Support vector machines

Multisurface support vector machine

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.