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2018 | OriginalPaper | Chapter

A Novel Nonlinear Multi-feature Fusion Algorithm: Multiple Kernel Multiset Integrated Canonical Correlation Analysis

Authors : Jing Yang, Liya Fan, Quansen Sun, Yuhua Fan

Published in: Cloud Computing and Security

Publisher: Springer International Publishing

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Abstract

Multiset integrated canonical correlation analysis (MICCA) can distinctly express the integral correlation among multi-group feature. Thus, MICCA is very powerful for multiple feature extraction. However, it is difficult to capture nonlinear relationships with the linear mapping. In order to overcome this problem, we, in this paper, propose a multi-kernel multiset integrated canonical correlation analysis (MK-MICCA) framework for subspace learning. In the MK-MICCA framework, the input data of each feature are mapped into multiple higher dimensional feature spaces by implicitly nonlinear mappings determined by different kernels. This enables MK-MICCA to uncover a variety of different geometrical structures of the original data in the feature spaces. Extensive experimental results on multiple feature database and ORL database show that MK-MICCA is very effective and obviously outperforms the single-kernel-based MICCA.

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Metadata
Title
A Novel Nonlinear Multi-feature Fusion Algorithm: Multiple Kernel Multiset Integrated Canonical Correlation Analysis
Authors
Jing Yang
Liya Fan
Quansen Sun
Yuhua Fan
Copyright Year
2018
Book
Cloud Computing and Security

Print ISBN: 978-3-030-00020-2

Electronic ISBN: 978-3-030-00021-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00021-9_24

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