Reducing the rank of relational factorization models by including observable patterns

Advances in Neural Information Processing Systems

Volumn 2, Issue January, 2014, Pages 1179-1187

  Nickel, Maximilian ^a,b   Jiang, Xueyan ^c,d   Tresp, Volker ^c,d  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

^c UNIVERSITY OF MUNICH   (Germany)

^d SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE; TENSORS;

FACTORIZATION MODEL; GENERALIZATION ABILITY; LATENT VARIABLE METHODS; MEMORY COMPLEXITY; PREDICTIVE PERFORMANCE; SCALABLE ALGORITHMS; TENSOR FACTORIZATION; UPPER AND LOWER BOUNDS;

FACTORIZATION;

EID: 84937929114     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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