A computationally efficient technique for feature extraction with null-space based linear discriminant analysis = 영 공간 기반 선형 판별 분석을 쓰는 특징 추출에서의 계산 효율적 기법

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The linear discriminant analysis (LDA), aiming at maximizing the ratio of the betweenclass distance to the within-class distance of data, is one of the most fundamental and powerful feature extraction methods. The LDA has been successfully applied in many applications such as facial recognition, text recognition, and image retrieval. However, due to the singularity of the within-class scatter, the LDA becomes ill-posed for small sample size (SSS) problems where the dimension of data is larger than the number of data. To extend the applicability of LDA in SSS problems, the null space-based LDA (NLDA) was proposed as an extension of the LDA. The NLDA has been shown in the literature to provide a good discriminant performance for SSS problems: Yet, as the original scheme for the feature extractor (FE) of the NLDA suffers from a complexity burden, a number of modified schemes based on QR factorization and eigen-decomposition have since been proposed for complexity reduction. In this dissertation, by transforming the problem of finding the FE of the NLDA into a linear equation problem, a novel scheme is derived, offering a further reduction of the complexity.
Advisors
Song, Iick-Horesearcher송익호
Description
한국과학기술원 : 전기및전자공학과,
Publisher
한국과학기술원
Issue Date
2012
Identifier
511887/325007  / 020054537
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전기및전자공학과, 2012.8, [ vi, 55 p. ]

Keywords

Complexity; Feature extractor; Linear equation problem; Null space based linear discriminant analysis; 복잡도; 특징 추출기; 선형 방정식 문제; 영 공간 기반 선형 판별 분석; 작은 표본 크기; Small sample size

URI
http://hdl.handle.net/10203/180168
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=511887&flag=dissertation
Appears in Collection
EE-Theses_Ph.D.(박사논문)
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