Similarity preserving principal curve: An optimal 1-D feature extractor for data representation

IEEE Transactions on Neural Networks

Volumn 21, Issue 9, 2010, Pages 1445-1456

  Sun, Mingming ^a   Yang, Jian ^a   Liu, Chuancai ^a   Yang, Jingyu ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Feature extraction; manifold learning; principal curve; reconstruction error; similarity preservation

Indexed keywords

DATA REPRESENTATIONS; DATA SETS; EVALUATION CRITERIA; FEATURE EXTRACTOR; HIGH NONLINEARITY; HIGH-DIMENSIONAL; INNER STRUCTURE; LEARNING MODELS; MANIFOLD LEARNING; NONLINEAR FEATURES; PRINCIPAL CURVE; RECONSTRUCTION ERROR; SIMILARITY PRESERVATION;

LEARNING ALGORITHMS; OPTIMIZATION; REPAIR;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; SIGNAL PROCESSING; STANDARD;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 77956343442     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2048577     Document Type: Article

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