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Low-Rank Kernel-Based Semisupervised Discriminant Analysis
Joint Authors
Zu, Baokai
Xia, Kewen
Dai, Shuidong
Aslam, Nelofar
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-20
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
Semisupervised Discriminant Analysis (SDA) aims at dimensionality reduction with both limited labeled data and copious unlabeled data, but it may fail to discover the intrinsic geometry structure when the data manifold is highly nonlinear.
The kernel trick is widely used to map the original nonlinearly separable problem to an intrinsically larger dimensionality space where the classes are linearly separable.
Inspired by low-rank representation (LLR), we proposed a novel kernel SDA method called low-rank kernel-based SDA (LRKSDA) algorithm where the LRR is used as the kernel representation.
Since LRR can capture the global data structures and get the lowest rank representation in a parameter-free way, the low-rank kernel method is extremely effective and robust for kinds of data.
Extensive experiments on public databases show that the proposed LRKSDA dimensionality reduction algorithm can achieve better performance than other related kernel SDA methods.
American Psychological Association (APA)
Zu, Baokai& Xia, Kewen& Dai, Shuidong& Aslam, Nelofar. 2016. Low-Rank Kernel-Based Semisupervised Discriminant Analysis. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1094895
Modern Language Association (MLA)
Zu, Baokai…[et al.]. Low-Rank Kernel-Based Semisupervised Discriminant Analysis. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1094895
American Medical Association (AMA)
Zu, Baokai& Xia, Kewen& Dai, Shuidong& Aslam, Nelofar. Low-Rank Kernel-Based Semisupervised Discriminant Analysis. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1094895
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094895