期刊
SIAM REVIEW
卷 51, 期 3, 页码 455-500出版社
SIAM PUBLICATIONS
DOI: 10.1137/07070111X
关键词
tensor decompositions; multiway arrays; multilinear algebra; parallel factors (PARAFAC); canonical decomposition (CANDECOMP); higher-order principal components analysis (Tucker); higher-order singular value decomposition (HOSVD)
资金
- Sandia National Laboratories
- United States Department of Energy [DE-AC04-94AL85000]
This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N-way array. Decompositions of higher-order tensors (i.e., N-way arrays with N >= 3) have applications in psychometrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.
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