A computationally efficient scheme for feature extraction with kernel discriminant analysis

被引：20

作者：

Min, Hwang-Ki ^{[1
]}

Hou, Yuxi ^{[1
]}

Park, Sangwoo ^{[1
]}

Song, Iickho ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon 305701, South Korea

来源：

PATTERN RECOGNITION | 2016年 / 50卷

基金：

新加坡国家研究基金会;

关键词：

Kernel discriminant analysis; Computational complexity; Lagrange method; Regularization; Pattern recognition; COMPONENT ANALYSIS; RECOGNITION; IMPLEMENTATION;

D O I：

10.1016/j.patcog.2015.08.021

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The kernel discriminant analysis (KDA), an extension of the linear discriminant analysis (LDA) and null space-based LDA into the kernel space, generally provides good pattern recognition (PR) performance for both small sample size (SSS) and non-SSS PR problems. Due to the eigen-decomposition technique adopted, however, the original scheme for the feature extraction with the KDA suffers from a high complexity burden. In this paper, we derive a transformation of the KDA into a linear equation problem, and propose a novel scheme for the feature extraction with the KDA. The proposed scheme is shown to provide us with a reduction of complexity without degradation of PR performance. In addition, to enhance the PR performance further, we address the incorporation of regularization into the proposed scheme. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：45 / 55

页数：11

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