Multi-view correlated feature learning by uncovering shared component

Xiaowei Xue, Feiping Nie, Sen Wang, Xiaojun Chang, Bela Stantic, Min Yao

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

20 Citations (Scopus)

Abstract

Learning multiple heterogeneous features from different data sources is challenging. One research topic is how to exploit and utilize the correlations among various features across multiple views with the aim of improving the performance of learning tasks, such as classification. In this paper, we propose a new multi-view feature learning algorithm that simultaneously analyzes features from different views. Compared to most of the existing subspace learning methods that only focus on exploiting a shared latent subspace, our algorithm not only learns individual information in each view but also captures feature correlations among multiple views by learning a shared component. By assuming that such a component is shared by all views, we simultaneously exploit the shared component and individual information of each view in a batch mode. Since the objective function is non-smooth and difficult to solve, we propose an efficient iterative algorithm for optimization with guaranteed convergence. Extensive experiments are conducted on several benchmark datasets. The results demonstrate that our proposed algorithm performs better than all the compared multi-view learning algorithms.

Original languageEnglish
Title of host publicationProceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
Subtitle of host publicationSan Francisco, California, USA — February 04 - 09, 2017
EditorsSatinder Singh, Shaul Markovitch
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages2810-2816
Number of pages7
Publication statusPublished - 2017
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2017 - Hilton San Francisco Union Square, San Francisco, United States of America
Duration: 4 Feb 201710 Feb 2017
Conference number: 31st
http://www.aaai.org/Conferences/AAAI/aaai17.php

Conference

ConferenceAAAI Conference on Artificial Intelligence 2017
Abbreviated titleAAAI 2017
Country/TerritoryUnited States of America
CitySan Francisco
Period4/02/1710/02/17
Internet address

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