title: Complexity-reduced scheme for feature extraction with linear discriminant analysis.
abstract: Owing to the singularity of the within-class scatter, linear discriminant analysis (LDA) becomes ill-posed for small sample size (SSS) problems. Null-space-based LDA (NLDA), which is an extension of LDA, provides good discriminant performances for SSS problems. Yet, as the original scheme for the feature extractor (FE) of NLDA suffers from a complexity burden, a few modified schemes have since been proposed for complexity reduction. In this brief, by transforming the problem of finding the FE of NLDA into a linear equation problem, a novel scheme is derived, offering a further reduction of the complexity.
authors.forename: Yuxi
authors.initials: Y
authors.lastname: Hou
authors.forename: Iickho
authors.initials: I
authors.lastname: Song
authors.forename: Hwang-Ki
authors.initials: HK
authors.lastname: Min
authors.forename: Cheol Hoon
authors.initials: CH
authors.lastname: Park