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Title: Making tensor factorizations robust to non-gaussian noise.

Tensors are multi-way arrays, and the CANDECOMP/PARAFAC (CP) tensor factorization has found application in many different domains. The CP model is typically fit using a least squares objective function, which is a maximum likelihood estimate under the assumption of independent and identically distributed (i.i.d.) Gaussian noise. We demonstrate that this loss function can be highly sensitive to non-Gaussian noise. Therefore, we propose a loss function based on the 1-norm because it can accommodate both Gaussian and grossly non-Gaussian perturbations. We also present an alternating majorization-minimization (MM) algorithm for fitting a CP model using our proposed loss function (CPAL1) and compare its performance to the workhorse algorithm for fitting CP models, CP alternating least squares (CPALS).
Authors:
;
Publication Date:
OSTI Identifier:
1011706
Report Number(s):
SAND2011-1877
TRN: US201109%%675
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Research Org:
Sandia National Laboratories
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; TENSORS; GAUSS FUNCTION; ALGORITHMS; FACTORIZATION; PERFORMANCE