Distance approximating dimension reduction of Riemannian manifolds

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 40, Issue 1, 2010, Pages 208-217

  Chen, Changyou ^a   Zhang, Junping ^a   Fleischer, Rudolf ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

Gait recognition; Linear discriminant analysis; Manifold learning; Multilinear tensor learning

Indexed keywords

BENCHMARK DATA; DATA POINTS; DATA SETS; DIMENSION REDUCTION; DISCRIMINATION ABILITY; FACE DATABASE; FACE RECOGNITION TECHNOLOGIES; GAIT RECOGNITION; GEODESIC DISTANCES; GRAPH DISTANCES; HIGH-DIMENSIONAL; HUMAN GAIT; ISOMETRIC EMBEDDINGS; LINEAR DISCRIMINANT ANALYSIS; LOW-DIMENSIONAL SUBSPACE; MANIFOLD LEARNING; NONLINEAR MANIFOLDS; OBJECTIVE FUNCTIONS; POTENTIAL ABILITY; RIEMANNIAN MANIFOLD; SOUTH FLORIDA; SYNTHETIC DATASETS; TWO-STAGE ALGORITHM; UNLABELED DATA; UPPER BOUND;

DISCRIMINANT ANALYSIS; GAIT ANALYSIS; GEODESY; LEARNING ALGORITHMS; OPTIMIZATION; TENSORS;

FACE RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CYBERNETICS; DISCRIMINANT ANALYSIS; FACE; FACTUAL DATABASE; GAIT; HISTOLOGY; HUMAN; METHODOLOGY; PHYSIOLOGY; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CYBERNETICS; DATABASES, FACTUAL; DISCRIMINANT ANALYSIS; FACE; GAIT; HUMANS; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 74049103739     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2009.2025028     Document Type: Article

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