Representation and feature selection using multiple kernel learning

Multiple kernel learning (MKL) approach for selecting and combining different representations of a data is presented. Selection of features from a representation of data using the MKL approach is also addressed. A base kernel function is used for each representation as well as for each feature from a representation. A new kernel is obtained as a linear combination of base kernels, weighted according to the relevance of representation or feature. The MKL approach helps to select and combine the representations as well as to select features from a representation. Issues in the MKL algorithm are addressed in the framework of support vector machines (SVM). Studies on the representation and feature selection are presented for an image categorization task.