Paper Info
Title
One-class support higher order tensor machine classifier.
Abstract
One-class classification problems have been widely encountered in the fields that the negative class patterns are difficult to be collected, and the one-class support vector machine is one of the popular algorithms for solving them. However, one-class support vector machine is a vector-based learning algorithm, and it cannot work directly when the input pattern is a tensor. This paper proposes a tensor-based maximum margin classifier for one-class classification problems, and develops a One-Class Support Higher Order Tensor Machine (HO-OCSTM) which can separate most of the target patterns from the origin with the maximum margin in the higher order tensor space. HO-OCSTM directly employs the higher order tensors as the input patterns, and it is more proper for small sample study. Moreover, the direct use of tensor representation has the advantage of retaining the structural information of data, which helps improve the generalization ability of the proposed algorithm. We implement HO-OCSTM by the alternating projection method and solve a convex quadratic programming similar to the standard one-class support vector machine algorithm at each iteration. The experimental results have shown the high recognition accuracy of the proposed method.
Year
DOI
Venue
2017
https://doi.org/10.1007/s10489-017-0945-9
Appl. Intell.
Keywords
Field
DocType
Support vector machine,One-class support vector machine,Support tensor machine,Higher order tensor,One-class classification
Structured support vector machine,One-class classification,Pattern recognition,Tensor,Computer science,Support vector machine,Projection method,Artificial intelligence,Relevance vector machine,Margin classifier,Classifier (linguistics),Machine learning
Journal
Volume
Issue
ISSN
47
4
0924-669X
Citations 
PageRank 
References 
1
0.35
16
Authors
3
Name
Order
Citations
PageRank
Yanyan Chen1163.59
Liyun Lu210.35
Ping Zhong34011.34