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Modifying Kernels Using Label Information Improves Protein Classification Performance
| Content Provider | Semantic Scholar |
|---|---|
| Author | Min, Renqiang Bonner, Anthony J. Zhang, Zhaolei |
| Abstract | Kernel learning methods based on kernel alignment with semidefinite programming (SDP) are often memory intensive and computationally expensive, thus often impractical for problems with large-size dataset. We propose a method using label information to scale the training part of a given kernel matrix to form the training part of a new kernel matrix. The test part of the new kernel matrix is estimated based on a linear transformation in a reduced feature space and can be calculated computationally efficiently. As a result, the new kernel matrix reflects the label-dependent separability of the sequence data in a better way than the original kernel matrix. In addition, our experimental results on a benchmark dataset, the SCOP dataset, show that the SVM classifier based on the improved kernels has better performance than the SVM classifier based on the original kernels; moreover, SVM based on the improved Profile kernel with pull-in homologs (see experiment section for explanations) produced the best results for remote homology detection on the SCOP dataset compared to the published results. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.cs.toronto.edu/~cuty/label.pdf |
| Alternate Webpage(s) | http://ftp.cs.toronto.edu/dist/cuty/labelinfo/labelinfo.pdf |
| Alternate Webpage(s) | http://www.cs.toronto.edu/~cuty/label.pdf |
| Alternate Webpage(s) | http://www.cs.toronto.edu/pub/cuty/labelinfo/labelinfo.pdf |
| Alternate Webpage(s) | http://ftp.cs.toronto.edu/pub/cuty/labelinfo/labelinfo.pdf |
| Alternate Webpage(s) | http://ftp.cs.toronto.edu/public_html/pub/cuty/labelinfo/labelinfo.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
