On utilizing search methods to select subspace dimensions for Kernel-Based nonlinear subspace classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 27, Issue 1, 2005, Pages 136-141

  Kim, Sang Woon ^a   Oommen, B John ^b  

^a Myong Ji University   (South Korea)

^b CARLETON UNIVERSITY   (Canada)

Author keywords

Kernel principal component analysis (kPCA); Kernel based nonlinear subspace (KNS) classifier; State space search algorithms; Subspace dimension selections

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; EIGENVALUES AND EIGENFUNCTIONS; HEURISTIC METHODS; MATRIX ALGEBRA; PRINCIPAL COMPONENT ANALYSIS; STATE SPACE METHODS;

KERNEL BASED NONLINEAR SUBSPACE (KNS) CLASSIFIER; KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA); STATE SPACE SEARCH ALGORITHMS; SUBSPACE DIMENSION SELECTIONS;

PATTERN RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; CLUSTER ANALYSIS; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; HEART ARRHYTHMIA; HUMAN; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; MATHEMATICAL COMPUTING; METHODOLOGY; NONLINEAR SYSTEM; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARRHYTHMIA; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; IMAGE ENHANCEMENT; INFORMATION STORAGE AND RETRIEVAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NONLINEAR DYNAMICS; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 13344282709     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2005.15     Document Type: Article

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