Title
On Robust Computation of Tensor Classifiers Based on the Higher-Order Singular Value Decomposition.
Abstract
In this paper a method of faster training of the ensembles of the tensor classifiers based on the Higher-Order Singular Value Decomposition is presented. The method relies on the fixed-point method of eigenvector computation which is employed at the stage of subspace construction of the flattened versions of the input tensor patterns. As verified experimentally, the proposed method allows up to five times speed-up factor at no significant difference in accuracy.
Year
DOI
Venue
2016
10.1007/978-3-319-33622-0_18
SOFTWARE ENGINEERING PERSPECTIVES AND APPLICATION IN INTELLIGENT SYSTEMS, VOL 2
Keywords
Field
DocType
Tensor classifiers,Subspace classification,Higher-Order singular value decomposition
Singular value decomposition,Applied mathematics,Subspace topology,Tensor,Mathematical analysis,Higher-order singular value decomposition,Eigenvalues and eigenvectors,Mathematics,Computation
Conference
Volume
ISSN
Citations 
465
2194-5357
3
PageRank 
References 
Authors
0.39
11
2
Name
Order
Citations
PageRank
Boguslaw Cyganek114524.53
Michal Wozniak276483.90