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Incremental Discriminant Analysis in Tensor Space
Joint Authors
Liu, Chang
Weidong, Zhao
Qiang, Pu
Xiaodan, Du
Yan, Tao
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
To study incremental machine learning in tensor space, this paper proposes incremental tensor discriminant analysis.
The algorithm employs tensor representation to carry on discriminant analysis and combine incremental learning to alleviate the computational cost.
This paper proves that the algorithm can be unified into the graph framework theoretically and analyzes the time and space complexity in detail.
The experiments on facial image detection have shown that the algorithm not only achieves sound performance compared with other algorithms, but also reduces the computational issues apparently.
American Psychological Association (APA)
Liu, Chang& Weidong, Zhao& Yan, Tao& Qiang, Pu& Xiaodan, Du. 2015. Incremental Discriminant Analysis in Tensor Space. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1057722
Modern Language Association (MLA)
Liu, Chang…[et al.]. Incremental Discriminant Analysis in Tensor Space. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1057722
American Medical Association (AMA)
Liu, Chang& Weidong, Zhao& Yan, Tao& Qiang, Pu& Xiaodan, Du. Incremental Discriminant Analysis in Tensor Space. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1057722
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057722