article

Multiple view semi-supervised dimensionality reduction

Authors:

Changshui Zhang,

Feiping NieAuthors Info & Claims

Pattern Recognition, Volume 43, Issue 3

Pages 720 - 730

https://doi.org/10.1016/j.patcog.2009.07.015

Published: 01 March 2010 Publication History

Abstract

Multiple view data, together with some domain knowledge in the form of pairwise constraints, arise in various data mining applications. How to learn a hidden consensus pattern in the low dimensional space is a challenging problem. In this paper, we propose a new method for multiple view semi-supervised dimensionality reduction. The pairwise constraints are used to derive embedding in each view and simultaneously, the linear transformation is introduced to make different embeddings from different pattern spaces comparable. Hence, the consensus pattern can be learned from multiple embeddings of multiple representations. We derive an iterating algorithm to solve the above problem. Some theoretical analyses and out-of-sample extensions are also provided. Promising experiments on various data sets, together with some important discussions, are also presented to demonstrate the effectiveness of the proposed algorithm.

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 43, Issue 3

March, 2010

655 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2009.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 March 2010

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Cited By

Tao HJiang JHou CLuo TFan RZhang J(2024)Compound Weakly Supervised ClusteringIEEE Transactions on Image Processing10.1109/TIP.2024.335410633(957-971)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3354106
Yang JXie KAn NZhang ARangwala H(2022)Causal Discovery on Non-Euclidean DataProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539485(2202-2211)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539485
Zhu CMa L(2021)Multi-view Multi-label Learning with Incomplete Views and LabelsSN Computer Science10.1007/s42979-021-00957-23:1Online publication date: 19-Nov-2021
https://dl.acm.org/doi/10.1007/s42979-021-00957-2
Xu RLi MYang ZYang LQiao KShang Z(2021)Dynamic feature selection algorithm based on Q-learning mechanismApplied Intelligence10.1007/s10489-021-02257-x51:10(7233-7244)Online publication date: 1-Oct-2021
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Tian QZhang WCao MWang LChen SYin H(2020)Moment-Guided Discriminative Manifold Correlation Learning on Ordinal DataACM Transactions on Intelligent Systems and Technology10.1145/340244511:5(1-18)Online publication date: 5-Jul-2020
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Zhuge WHou CPeng SYi D(2020)Joint consensus and diversity for multi-view semi-supervised classificationMachine Language10.1007/s10994-019-05844-9109:3(445-465)Online publication date: 1-Mar-2020
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Zhu CWang PMa LZhou RWei L(2020)Global and local multi-view multi-label learning with incomplete views and labelsNeural Computing and Applications10.1007/s00521-020-04854-232:18(15007-15028)Online publication date: 1-Sep-2020
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Zhuge WHou CLiu XTao HYi D(2019)Simultaneous representation learning and clustering for incomplete multi-view dataProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367665(4482-4488)Online publication date: 10-Aug-2019
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