Using Kernel Basis with Relevance Vector Machine for Feature Selection

This paper presents an application of multiple kernels like Kernel Basis to the Relevance Vector Machine algorithm. The framework of kernel machines has been a source of many works concerning the merge of various kernels to build the solution. Within these approaches, Kernel Basis is able to combine both local and global kernels. The interest of such approach resides in the ability to deal with a large kind of tasks in the field of model selection, for example the feature selection. We propose here an application of RVM-KB to a feature selection problem, for which all data are decomposed into a set of kernels so that all points of the learning set correspond to a single feature of one data. The final result is the selection of the main features through the relevance vectors selection.

• Type: Conference
• Year: 2009
• Where: ICANN
• Authors: Frederic Suard, David Mercier
• Keywords: Artificial Intelligence, Feature Selection, ICANN 2009, Kernel Basis, Relevance Vectors