[BANANA] Seminar: Wed Oct 4

[Gene H Golub]

Linear Algebra and Optimization Seminar
Fall 2006
Gates Building, Room 104
Morten Mrup
Wednesday, October 4, 4:15pm
Extensions of Non-Negative Matrix Factorization (NMF) to Higher Order Data

Higher order matrix (tensor) decompositions are mainly used in 
psychometrics, chemometrics, image analysis, graph analysis and signal 
processing. For higher order data the two most commonly used 
decompositions are the PARAFAC and the TUCKER model. If the data analyzed 
is non-negative it may be relevant to consider additive non-negative 
components. We here extend non-negative matrix factorization (NMF) to form 
algorithms for non-negative TUCKER and PARAFAC decompositions. 
Furthermore, we extend the PARAFAC model to account for shift and echo 
effects in the data. To improve uniqueness of the decompositions we use 
updates that can impose sparseness in any combination of modalities. The 
algorithms developed are demonstrated on a range of datasets spanning from 
electroencephalography to sound and chemometry signals.


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PLEASE SEND ALL FUTURE MAIL TO ME AT
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Thanks. Gene



Gene Golub,  Fletcher Jones Professor of Computer Science
Gates 2B
Computer Science Dept
Stanford University
Stanford, CA 94305
USA

Office Phone: 650 723 3124
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