Simple but effective methods for combining kernels in computational biology

Complex biological data generated from various experiments are stored in diverse data types in multiple datasets. By appropriately representing each biological dataset as a kernel matrix then combining them in solving problems, the kernelbased approach has become a spotlight in data integration and its application in bioinformatics and other fields as well. While linear combination of unweighed multiple kernels (UMK) is popular, there have been effort on multiple kernel learning (MKL) where optimal weights are learned by semi-definite programming or sequential minimal optimization (SMO-MKL). These methods provide high accuracy of biological prediction problems, but very complicated and hard to use, especially for non-experts in optimization. These methods are also usually of high computational cost and not suitable for large data sets. In this paper, we propose two simple but effective methods for determining weights for conic combination of multiple kernels. The former is to learn opt...

• Type: Conference
• Year: 2008
• Where: RIVF
• Authors: Hiroaki Tanabe, Tu Bao Ho, Canh Hao Nguyen, Saori Kawasaki
• Keywords: Internet Technology, Kernel Matrix, Kernel Matrix Evaluation, Multiple Kernels, RIVF 2008