Penalized preimage learning in kernel principal component analysis

IEEE Transactions on Neural Networks

Volumn 21, Issue 4, 2010, Pages 551-570

  Zheng, Wei Shi ^a,b   Lai, Jianhuang ^a   Yuen, Pong C ^c  

^a SUN YAT SEN UNIVERSITY   (China)

^b QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^c HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Kernel; Kernel principal component analysis (KPCA); Locality preservation; Penalty function; Preimage problem

Indexed keywords

COMBINATION WEIGHTS; CONDITIONAL MUTUAL INFORMATION; CONVEXITY CONSTRAINT; DATA RANGES; FACE IMAGES; FACIAL EXPRESSIONS; FEATURE SPACE; FEATURE VECTORS; ILL POSED; ILLUMINATION NORMALIZATION; IMAGE PREPROCESSING; INPUT DATAS; KERNEL METHODS; KERNEL PRINCIPAL COMPONENT ANALYSIS; LAPLACIAN PENALTY; LEARNING PROCESS; OBSERVED SAMPLES; OPEN PROBLEMS; OPTIMIZATION FUNCTION; PENALIZED FUNCTIONS; PENALTY FUNCTION; PRE-IMAGE PROBLEM; PREIMAGES; RECONSTRUCTED IMAGE; RIDGE PENALTY; VISUAL QUALITIES;

ESTIMATION; FACE RECOGNITION; FEATURE EXTRACTION; LEARNING ALGORITHMS; MEAN SQUARE ERROR; OPTIMIZATION; VECTOR SPACES;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS;

EID: 77950860472     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2009.2039647     Document Type: Article

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