Local learning rules for nonnegative Tucker decomposition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5863 LNCS, Issue PART 1, 2009, Pages 538-545

  Phan, Anh Huy ^a   Cichocki, Andrzej ^a,b  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^b WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF DATA; CHEMOMETRICES; COMPUTATIONAL NEUROSCIENCE; CONSTRAINED MINIMIZATION; DATA ANALYSIS; HIGH DIMENSIONALITY; LARGE-SCALE PROBLEM; LOCAL LEARNING; MATRIX FACTORIZATIONS; MODERN APPLICATIONS; NONNEGATIVE TUCKER DECOMPOSITIONS; NONNEGATIVITY CONSTRAINTS; POTENTIAL APPLICATIONS; SPECTRAL ANALYSIS; TEXT MINING;

COST FUNCTIONS; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS;

DATA HANDLING;

EID: 76649109898     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10677-4_61     Document Type: Conference Paper

References (12)

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5. Phan, A.H., Cichocki, A.: Fast and efficient algorithms for nonnegative Tucker decomposition. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds.) ISNN 2008, Part II. LNCS, vol. 5264, pp. 772-782. Springer, Heidelberg (2008)
6. Cichocki, A., Phan, A.H.: Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE (invited paper) (March 2009)
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9. Bader, B., Kolda, T.: MATLAB Tensor Toolbox Version 2.2 (January 2007)
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11. Phan, A.H., Cichocki, A.: Analysis of interactions among hidden components for Tucker model. In: APSIPA Annual Summit and Conference (2009)
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