Feature space perspectives for learning the kernel

Feature space perspectives for learning the kernel1

In this paper, we continue our study of learning the kernel. We present a reformulation of this problem within a feature space environment. This leads us to study regularization in the dual space of all continuous functions on a compact domain with values in a Hilbert space with a mix norm. We also relate this problem in a special case to regularization. 1This work was supported by NSF Grant IT...

Feature and kernel learning

Feature selection and weighting has been an active research area in the last few decades finding success in many different applications. With the advent of Big Data, the adequate identification of the relevant features has converted feature selection in an even more indispensable step. On the other side, in kernel methods features are implicitly represented by means of feature mappings and kern...

Constructing Kernel Machines in the Empirical Kernel Feature Space

Feature Extraction for Multiple Kernel Learning

Multiple Kernel Learning (MKL) synthesizes a single kernel from a set of multiple kernels for use in a support vector machine. We propose that MKL be preceded by feature extraction. Given a set of kernels and a vector y of class labels, Multiple Kernel Basis Extraction (MKBE) constructs orthogonal vectors {v1, . . . , vm} whose corresponding kernels, {v1v 1 , . . . , vmv m}, are maximally align...

Quantized Kernel Learning for Feature Matching

Matching local visual features is a crucial problem in computer vision and its accuracy greatly depends on the choice of similarity measure. As it is generally very difficult to design by hand a similarity or a kernel perfectly adapted to the data of interest, learning it automatically with as few assumptions as possible is preferable. However, available techniques for kernel learning suffer fr...