Linear and nonlinear projective nonnegative matrix factorization

IEEE Transactions on Neural Networks

Volumn 21, Issue 5, 2010, Pages 734-749

  Yang, Zhirong ^a   Oja, Erkki ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Clustering; Kernel; Multiplicative updates; Nonnegative matrix factorization (NMF); Projection recovery; Projective

Indexed keywords

BINARY CONSTRAINTS; CLUSTERING APPROACH; DATA MATRICES; DISCRETIZATIONS; EMPIRICAL STUDIES; FROBENIUS MATRICES; GRAPH PARTITIONING PROBLEMS; HIGH DIMENSIONAL DATA; KERNEL; KERNEL PRINCIPAL COMPONENT ANALYSIS; KULLBACK LEIBLER DIVERGENCE; LAGRANGIAN MULTIPLIERS; LOW-RANK MATRICES; MULTIPLICATIVE UPDATES; NONLINEAR KERNELS; NONNEGATIVE MATRIX FACTORIZATION; PROJECTION MATRIX; PROJECTIVE NONNEGATIVE MATRIX FACTORIZATION; REAL-WORLD DATABASE; RECONSTRUCTION ERROR;

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; FACTORIZATION; LIGHT MEASUREMENT; METAL RECOVERY; PRINCIPAL COMPONENT ANALYSIS;

MATRIX ALGEBRA;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOMETRY; CLUSTER ANALYSIS; HUMAN; METHODOLOGY; NONLINEAR SYSTEM; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMETRY; CLUSTER ANALYSIS; HUMANS; LINEAR MODELS; NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS;

EID: 77951938107     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2041361     Document Type: Article

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