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2019 | OriginalPaper | Chapter

Efficient Graph Based Multi-view Learning

Authors : Hengtong Hu, Richang Hong, Weijie Fu, Meng Wang

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

Graph-based learning methods especially multi-graph-based methods have attracted considerable research interests in the past decades. In these methods, the traditional graph models are used to build adjacency relationships for samples within different views. However, owing to the huge time complexity, they are inefficient for large-scale datasets. In this paper, we propose a method named multi-anchor-graph learning (MAGL), which aims to utilize anchor graphs for the adjacency estimation. MAGL can not only sufficiently explore the complementation of multiple graphs built upon different views but also keep an acceptable time complexity. Furthermore, we show that the proposed method can be implemented through an efficient iterative process. Extensive experiments on six publicly available datasets have demonstrated both the effectiveness and efficiency of our proposed approach.

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Metadata
Title
Efficient Graph Based Multi-view Learning
Authors
Hengtong Hu
Richang Hong
Weijie Fu
Meng Wang
Copyright Year
2019
Book
MultiMedia Modeling

Print ISBN: 978-3-030-05709-1

Electronic ISBN: 978-3-030-05710-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-05710-7_57