Name
Abstract | ||
|---|---|---|
Graphical abstractIllustration of the decision hyperplanes generated by TSSVM, MCVSVM, and LMLP on an artificial dataset. Display Omitted HighlightsIn the case of the singularity of the within-class scatter matrix, the drawbacks of both MCVSVM and LMLP are analyzed.A novel algorithm TSSVM is proposed to deal with the high-dimensional data classification task where the within-class scatter matrix is singular.An alternative version of the nonlinear MCVSVM and the nonlinear LMLP are proposed.The nonlinear TSSVM is developed. Minimum class variance support vector machine (MCVSVM) and large margin linear projection (LMLP) classifier, in contrast with traditional support vector machine (SVM), take the distribution information of the data into consideration and can obtain better performance. However, in the case of the singularity of the within-class scatter matrix, both MCVSVM and LMLP only exploit the discriminant information in a single subspace of the within-class scatter matrix and discard the discriminant information in the other subspace. In this paper, a so-called twin-space support vector machine (TSSVM) algorithm is proposed to deal with the high-dimensional data classification task where the within-class scatter matrix is singular. TSSVM is rooted in both the non-null space and the null space of the within-class scatter matrix, takes full advantage of the discriminant information in the two subspaces, and so can achieve better classification accuracy. In the paper, we first discuss the linear case of TSSVM, and then develop the nonlinear TSSVM. Experimental results on real datasets validate the effectiveness of TSSVM and indicate its superior performance over MCVSVM and LMLP. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.asoc.2015.10.051 | Applied Soft Computing |
Keywords | Field | DocType |
Machine learning,Supervised learning,Kernel methods,Support vector machine | Structured support vector machine,Artificial intelligence,Scatter matrix,Kernel (linear algebra),Pattern recognition,Support vector machine,Algorithm,Linear subspace,Relevance vector machine,Linear classifier,Kernel method,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
40 | C | 1568-4946 |
Citations | PageRank | References |
7 | 0.44 | 29 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Wang | 1 | 217 | 16.55 |
| Shitong Wang | 2 | 1485 | 109.13 |