Name
Abstract | ||
|---|---|---|
It is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning framework for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11758525_93 | International Conference on Computational Science (2) |
Keywords | Field | DocType |
large set,plasmodium falciparum expression profile,nonlinearly separable data,microarray gene expression data,small sample size,kernel algorithm,self-supervised learning framework,unlabeled data,linear algorithm,microarray data,supervised learning | Data mining,Semi-supervised learning,Computer science,Artificial intelligence,Small set,Kernel (linear algebra),Mathematical optimization,Pattern recognition,Subspace topology,Expectation–maximization algorithm,Curse of dimensionality,Supervised learning,DNA microarray | Conference |
Volume | ISSN | ISBN |
3992 | 0302-9743 | 3-540-34381-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yijuan Lu | 1 | 732 | 46.24 |
| Qi Tian | 2 | 6443 | 331.75 |
| Feng Liu | 3 | 0 | 0.34 |
| Maribel Sanchez | 4 | 0 | 0.34 |
| Yufeng Wang | 5 | 51 | 14.37 |