Paper Info
Title
Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries.
Abstract
We consider $N$-way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library. Our method, Shape Constrained Tensor Decomposition (SCTD) is based upon the CANDECOMP/PARAFAC (CP) decomposition which produces $r$-rank approximations of data tensors via outer products of vectors in each dimension of the data. By constraining the vector in the temporal dimension to known analytic forms which are selected from a large set of candidate functions, more readily interpretable decompositions are achieved and analytic time dependencies discovered. The SCTD method circumvents traditional {em flattening} techniques where an $N$-way array is reshaped into a matrix in order to perform a singular value decomposition. A clear advantage of the SCTD algorithm is its ability to extract transient and intermittent phenomena which is often difficult for SVD-based methods. We motivate the SCTD method using several intuitively appealing results before applying it on a number of high-dimensional, real-world data sets in order to illustrate the efficiency of the algorithm in extracting interpretable spatio-temporal modes. With the rise of data-driven discovery methods, the decomposition proposed provides a viable technique for analyzing multitudes of data in a more comprehensible fashion.
Year
Venue
Field
2016
arXiv: Machine Learning
Linear combination,Singular value decomposition,Mathematical optimization,Data set,Flattening,Tensor,Matrix (mathematics),Approximations of π,Artificial intelligence,Machine learning,Mathematics,Tensor decomposition
DocType
Volume
Citations 
Journal
abs/1608.04674
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Bethany Lusch100.68
Eric C. Chi2936.89
J. Nathan Kutz322547.13