Multiple kernel learning

About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.

A Binary Classification Framework for Two-Stage Multiple Kernel Learning

Abstract: With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of pre-specified base kernels that is suitable for the task at hand has received significant attention from researchers. In this paper we show that Multiple Kernel Learning can be framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Framing MKL in this way has the distinct advantage that it makes it easy to leverage the extensive research in binary classification to develop better performing and more scalable MKL algorithms that are conceptually simpler, and, arguably, more accessible to practitioners. Experiments on nine data sets from different domains show that, despite its simplicity, the proposed technique compares favorably with current leading MKL approaches.

Unsupervised Multiple Kernel Learning

Abstract: Traditional multiple kernel learning (MKL) algorithms are essentially supervised learning in the sense that the kernel learning task requires the class labels of training data. However, class labels may not always be available prior to the kernel learning task in some real world scenarios, e.g., an early preprocessing step of a classification task or an unsupervised learning task such as dimension reduction. In this paper, we investigate a problem of Unsupervised Multiple Kernel Learning (UMKL), which does not require class labels of training data as needed in a conventional multiple kernel learning task. Since a kernel essentially defines pairwise similarity between any two examples, our unsupervised kernel learning method mainly follows two intuitive principles: (1) a good kernel should allow every example to be well reconstructed from its localized bases weighted by the kernel values; (2) a good kernel should induce kernel values that are coincided with the local geometry of the data. We formulate the unsupervised multiple kernel learning problem as an optimization task and propose an efficient alternating optimization algorithm to solve it. Empirical results on both classification and dimension reductions tasks validate the efficacy of the proposed UMKL algorithm.

Machine Learning Approaches for the Neuroimaging Study of Alzheimer's Disease

Abstract: Machine learning tools aid many Alzheimer's disease-related investigations by enabling multisource data fusion and biomarker identification as well as analysis of functional brain connectivity.