Multi-view kernel machine on single-view data

doi:10.1016/j.neucom.2008.11.017

2009-06-01

PRE

王哲 (Zhe Wang)

Songcan Chen *

DOI：10.1016/j.neucom.2008.11.017

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摘要

En 中文

Existing multi-view learning focuses on the problem of how to learn from data represented by multiple independent sets of attributes (termed as multi-view data), and has been proved to bring an excellent performance. However, in general, we have only a single set of attributes (termed as single-view data) available. The goal of this paper is to employ the multi-view viewpoint to develop a multi-view kernel machine for such a single-view data. The key of doing so is to associate each learning machine with one kernel, take it as one view and thus form a set of learning machines from their corresponding kernels, as a result, a multi-view kernel machine can be developed by synthesizing them into a single learning framework. Further, in the two-view (two-kernel) case, we explore the relationship between the generalization ability of the proposed kernel machine and its associated kernels, in which with the kernel alignment (KA) as a correlation measure between kernels, it is found that superior performance of the proposed machine results from a weaker correlation between the constitutive kernels. To the best of our knowledge, both the multi-view learning on single-view data and the KA measure used here have not appeared in any literature. In practice, we take the kernel modified Ho-Kashyap with squared (KMHKS) approximation of the misclassification errors as a learning machine to develop a multi-view KMHKS (MultiV-KMHKS) on single-view data. Crown Copyright (c) 2009 Published by Elsevier B.V. All rights reserved.

Keyword:

Multi-view learning

Single-view data

Modified Ho-Kashyap algorithm

Kernel alignment

Pattern recognition

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期刊

Neurocomputing

IF:

6.5

论文数:

2.5W

被引数:

6.5W

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