CPOPT : optimization for fitting CANDECOMP/PARAFAC models.

Conference · Wed Oct 01 00:00:00 EDT 2008

OSTI ID:970228

Dunlavy, Daniel M; Kolda, Tamara Gibson; Acar, Evrim

Tensor decompositions (e.g., higher-order analogues of matrix decompositions) are powerful tools for data analysis. In particular, the CANDECOMP/PARAFAC (CP) model has proved useful in many applications such chemometrics, signal processing, and web analysis; see for details. The problem of computing the CP decomposition is typically solved using an alternating least squares (ALS) approach. We discuss the use of optimization-based algorithms for CP, including how to efficiently compute the derivatives necessary for the optimization methods. Numerical studies highlight the positive features of our CPOPT algorithms, as compared with ALS and Gauss-Newton approaches.

• Research Organization: Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 970228
• Report Number(s): SAND2008-6868C; TRN: US201003%%446
• Resource Relation: Conference: Proposed for presentation at the Computational Algebraic Statistics, Theories and Applications (CASTA2008) held December 10-11, 2008 in Kyoto, Japan.
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ALGORITHMS
DATA ANALYSIS
OPTIMIZATION
PROCESSING
STATISTICS