Open AccessIncremental null Foley‐Sammon transform
Author(s) -
Wang Yi,
Shui Panpan,
Fan Xin,
Wang Tianzhu
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2015.2053
Subject(s) - dimensionality reduction , artificial intelligence , pattern recognition (psychology) , curse of dimensionality , computer science , feature extraction , matrix (chemical analysis) , mathematics , chromatography , chemistry
The Foley‐Sammon transform (FST) is one of the most well‐known dimensionality reduction and feature extraction methods. However, the classical FST cannot be used directly in the small sample size problem where the within‐class scatter matrix is singular. Null‐space based FST (NFST) provides a good solution to this problem. Proposed is a fast incremental NFST (INFST). INFST extracts new information brought by newly‐added samples and integrates it with the existing model by an efficient updating scheme. INFST can achieve the aims of online classification and novelty detection. Experiments on real‐world data demonstrate the effectiveness of INFST.