Abstract Details
|
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.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.