Name
Abstract | ||
|---|---|---|
The classification performance using support vector machines (SVMs) for transcriptomic analysis can be limited due to the high dimensionality of the data. This limitation is most problematic in the case of small training sets. A general solution is to employ a dimension reduction method before SVM classification. In this paper, we propose a novel singular value decomposition (SVD) based method for dual purposes: firstly, to reduce the dimensionality, and secondly to cluster the transcriptional profiles. The kernel functions of SVM were modified based on the Riemannian geometrical structure which can achieve a better spatial resolution. The proposed approach was applied to the yeast time series microarray dataset and outperformed the traditional SVM kernels. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/GENSiPS.2011.6169476 | GENSiPS |
Keywords | Field | DocType |
pattern clustering,transcriptional profile clustering,svm classification,pattern classification,yeast time series microarray dataset,svm kernels,biology computing,singular value decomposition based method,svd clustering,transcriptomic analysis,dimension reduction method,classification performance,riemannian geometrical structure,rna,time series,singular value decomposition,support vector machines,kernel,spatial resolution,matrix decomposition,polynomials,support vector machine,kernel function,biology,dimension reduction | Data mining,Dimensionality reduction,Computer science,Artificial intelligence,Cluster analysis,Kernel (linear algebra),Singular value decomposition,Pattern recognition,Matrix decomposition,Support vector machine,Curse of dimensionality,Machine learning,Kernel (statistics) | Conference |
ISSN | ISBN | Citations |
2150-3001 E-ISBN : 978-1-4673-0489-4 | 978-1-4673-0489-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Cai | 1 | 65 | 11.01 |
| Yufeng Wang | 2 | 51 | 14.37 |