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Activity Number: 52
Type: Invited
Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract - #307137
Title: Non-Negative Tensor Factorization: Algorithms and Applications
Author(s): Eric Chi*+
Companies: UCLA
Keywords: Tensors ; CANDECOMP/PARAFAC ; Multiway ; Nonnegative
Abstract:

Tensors have found application in a variety of fields, including signal processing, chemometrics, bioinformatics, and neuroimaging. In the latter three examples, data is nonnegative and estimating nonnegative multilinear models can yield more interpretable underlying physical model by representing the data as a sum of nonnegative components or parts. In this work we consider a nonnegative CANDECOMP/PARAFAC (CP) decomposition of a tensor of count data. We present fast and scalable algorithm for maximum likelihood estimation as well as a novel cross-validation scheme for choosing the rank of the CP model. The effectiveness of our approach for identifying underlying trends is demonstrated on simulated data and crime data.


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