Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning

IEEE Transactions on Neural Networks

Volumn 20, Issue 11, 2009, Pages 1820-1836

  Lu, Haiping ^a,b   Plataniotis, Konstantinos N ^a   Venetsanopoulos, Anastasios N ^a,c  

^a UNIVERSITY OF TORONTO   (Canada)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^c RYERSON UNIVERSITY   (Canada)

Author keywords

Dimensionality reduction; Face recognition; Feature extraction; Gait recognition; Multilinear principal component analysis (MPCA); Tensor objects; Uncorrelated features

Indexed keywords

ALGORITHMS; FACE RECOGNITION; FEATURE EXTRACTION; GAIT ANALYSIS; ITERATIVE METHODS; PATTERN RECOGNITION; TENSORS; VECTORS;

DIMENSIONALITY REDUCTION; GAIT RECOGNITION; MULTILINEAR PRINCIPAL COMPONENT ANALYSIS (MPCA); TENSOR OBJECTS; UNCORRELATED FEATURES;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; GAIT; METHODOLOGY; MOVEMENT PERCEPTION; PATTERN RECOGNITION; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MODEL; VISION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GAIT; LINEAR MODELS; MOTION PERCEPTION; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; PRINCIPAL COMPONENT ANALYSIS; VISUAL PERCEPTION;

EID: 75549085623     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2009.2031144     Document Type: Article

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