Nonnegative contraction/averaging tensor factorization

Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of negative matrix factorization (NMF), where nonnegativity constraints are mainly imposed on CANDECOMP/PARAFAC model and recently, also, on Tucker model. Nonnegative tensor factorization algorithms have many potenti...

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Bibliographic Details
Main Authors: Jankovic, M V, Reljin, B
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Arrays
Brain modeling
Data analysis
Data models
nonnegative tensor factorization
PARFAC model
Tensile stress
Three dimensional displays
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Summary:Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of negative matrix factorization (NMF), where nonnegativity constraints are mainly imposed on CANDECOMP/PARAFAC model and recently, also, on Tucker model. Nonnegative tensor factorization algorithms have many potential applications, including multiway clustering, multi-sensory or multidimensional data analysis and nonnegative neural sparse coding. In this paper we will present new approach to NTF which is based on CANDENCOMP/PARAFAC model. The proposed method is simple, computationally effective, easily extensible to higher dimensional tensors, can handle some problems related to rank-deficient tensors and can be used for analysis of the higher dimensional tensors than most of the known algorithms for NTF.
DOI:10.1109/NEUREL.2010.5644083