Paper Info
Title
Efficient Multidimensional Pattern Recognition In Kernel Tensor Subspaces
Abstract
In this paper we discuss algorithmically efficient methods of multidimensional patter recognition in kernel tensor subspaces. The kernel principal component analysis, which originally operates only on vector data, is joined with the tensor chordal kernel which opens a way of direct usage of the multidimensional signals, such as color video streams, seismic signals or hyper-spectral images. We address the problem of efficient implementation of the eigendecomposition problem which is a core algorithm for both methods. For this the fixed point algorithm is employed. We show usefulness of this approach on the problem of visual pattern recognition and show speed-up ratio when using the proposed implementation.
Year
DOI
Venue
2016
10.1007/978-3-319-40973-3_54
DATA MINING AND BIG DATA, DMBD 2016
Keywords
Field
DocType
Kernel PCA, Chordal kernel, Tensor, Subspace classification
Kernel (linear algebra),Tensor,Pattern recognition,Computer science,Chordal graph,Fixed point algorithm,Hyperspectral imaging,Linear subspace,Kernel principal component analysis,Eigendecomposition of a matrix,Artificial intelligence
Conference
Volume
ISSN
Citations 
9714
0302-9743
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Boguslaw Cyganek114524.53
Michał Woźniak221324.64