Title
Low Rank Tensor Deconvolution
Abstract
In this paper, we propose a low-rank tensor deconvolution problem which seeks multiway replicative patterns and corresponding activating tensors of rank-1. An alternating least squares (ALS) algorithm has been derived for the model to sequentially update loading components and the patterns. In addition, together with a good initialisation method using tensor diagonalization, the update rules have been implemented with a low cost using fast inversion of block Toeplitz matrices as well as an efficient update strategy. Experiments show that the proposed model and the algorithm are promising in feature extraction and clustering.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP)
tensor decomposition, tensor deconvolution, tensor diagonalization, CANDECOMP/PARAFAC
Field
DocType
ISSN
Mathematical optimization,Tensor,Computer science,Matrix (mathematics),Matrix decomposition,Deconvolution,Toeplitz matrix,Stress (mechanics),Feature extraction,Cluster analysis
Conference
1520-6149
Citations 
PageRank 
References 
2
0.37
9
Authors
3
Name
Order
Citations
PageRank
Anh Huy Phan182851.60
Petr Tichavský234141.01
Andrzej Cichocki35228508.42