low-rank kernel-based semisupervised discriminant analysis
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ID: 218214
2016
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Abstract
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.
| Reference Key |
zu2016appliedlow-rank
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|---|---|
| Authors | ;Baokai Zu;Kewen Xia;Shuidong Dai;Nelofar Aslam |
| Journal | journal of evidence-based complementary & alternative medicine |
| Year | 2016 |
| DOI |
10.1155/2016/2783568
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| URL | |
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