Linear classifier design in the weight space. (April 2019)

• Record Type: Journal Article
• Authors: Lee, Chulhee; Woo, Seongyoun
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 210
• Page End: 222
• Publication Date: 2019-04
• Subjects: Interior point -- Linear classifier -- Optimal linear classifier -- Pyramid subspace -- Weight space; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.11.024
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a linear classifier design method in the weight space based on the observation that a training sample represents a plane in the weight space. We proposed search algorithms to find the optimal subspace, though the optimal solution may not be guaranteed since the full search cannot be used. Experimental results show that the proposed classifier performed better than or equivalent to existing linear classifiers (linear SVM, LDA). Abstract: In this paper, we propose a linear classifier design method in the weight space. A linear classifier is completely determined by a weight vector. To design a linear classifier is equivalent to finding a weight vector. When there are a number of training samples, each training sample represents a plane in the weight space. On one side of the plane, the training sample is correctly classified while it is incorrectly classified on the other side. Thus, finding the optimal linear classifier can be formulated as finding a subspace that provides the best classification accuracy. In this paper, we have developed an algorithm to find such optimal subspaces in the weight space. Although we could not find the optimal linear classifier due to prohibitive computational complexity, the proposed design method produced noticeable improvements in some cases. Experimental results show that the proposed linear classifier performed better than or equivalent to existing linear classifiers.