Complexity-reduced scheme for feature extraction with linear discriminant analysis.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 24806770.
- Also identified by DOI 10.1109/TNNLS.2012.2194793.
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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.