CPOPT : optimization for fitting CANDECOMP/PARAFAC models.
Conference
·
OSTI ID:970228
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
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 970228
- Report Number(s):
- SAND2008-6868C
- Country of Publication:
- United States
- Language:
- English
Similar Records
An optimization approach for fitting canonical tensor decompositions.
Scalable tensor factorizations with incomplete data.
Scalable tensor factorizations with missing data.
Technical Report
·
Sat Jan 31 23:00:00 EST 2009
·
OSTI ID:978916
Scalable tensor factorizations with incomplete data.
Conference
·
Thu Jul 01 00:00:00 EDT 2010
·
OSTI ID:1021587
Scalable tensor factorizations with missing data.
Conference
·
Thu Apr 01 00:00:00 EDT 2010
·
OSTI ID:1017019