research-article

Multi-View Concept Learning for Data Representation

Authors:

Jianping FanAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 27, Issue 11

Pages 3016 - 3028

https://doi.org/10.1109/TKDE.2015.2448542

Published: 01 November 2015 Publication History

Abstract

Real-world datasets often involve multiple views of data items, e.g., a Web page can be described by both its content and anchor texts of hyperlinks leading to it; photos in Flickr could be characterized by visual features, as well as user contributed tags. Different views provide information complementary to each other. Synthesizing multi-view features can lead to a comprehensive description of the data items, which could benefit many data analytic applications. Unfortunately, the simple idea of concatenating different feature vectors ignores statistical properties of each view and usually incurs the “curse of dimensionality” problem. We propose Multi-view Concept Learning (MCL), a novel nonnegative latent representation learning algorithm for capturing conceptual factors from multi-view data. MCL exploits both multi-view information and label information. The key idea is to learn a common latent space across different views which (1) captures the semantic relationships between data items through graph embedding regularization on labeled items, and (2) allows each latent factor to be associated with a subset of views via sparseness constraints. In this way, MCL could capture flexible conceptual patterns hidden in multi-view features. Experiments on a toy problem and three real-world datasets show that MCL performs well and outperforms baseline methods.

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Cited By

Tao YChe HLi CPan BLeung M(2025)Weight consistency and cluster diversity based concept factorization for multi-view clusteringDigital Signal Processing10.1016/j.dsp.2024.104879157:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.dsp.2024.104879
Yu ZDong ZYu CYang KFan ZChen C(2025)A review on multi-view learningFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40004-w19:7Online publication date: 1-Jul-2025
https://dl.acm.org/doi/10.1007/s11704-024-40004-w
Wang DLi TDeng PLuo ZZhang PLiu KHuang W(2024)DNSRF: Deep Network-based Semi-NMF Representation FrameworkACM Transactions on Intelligent Systems and Technology10.1145/367040815:5(1-20)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.1145/3670408
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Index Terms

Multi-View Concept Learning for Data Representation
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 27, Issue 11

Nov. 2015

287 pages

ISSN:1041-4347

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Copyright © 2015.

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IEEE Educational Activities Department

United States

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Published: 01 November 2015

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Cited By

Tao YChe HLi CPan BLeung M(2025)Weight consistency and cluster diversity based concept factorization for multi-view clusteringDigital Signal Processing10.1016/j.dsp.2024.104879157:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.dsp.2024.104879
Yu ZDong ZYu CYang KFan ZChen C(2025)A review on multi-view learningFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40004-w19:7Online publication date: 1-Jul-2025
https://dl.acm.org/doi/10.1007/s11704-024-40004-w
Wang DLi TDeng PLuo ZZhang PLiu KHuang W(2024)DNSRF: Deep Network-based Semi-NMF Representation FrameworkACM Transactions on Intelligent Systems and Technology10.1145/367040815:5(1-20)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.1145/3670408
Guo DXu WQian YDing W(2024)Fuzzy-Granular Concept-Cognitive Learning via Three-Way Decision: Performance Evaluation on Dynamic Knowledge DiscoveryIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.332595232:3(1409-1423)Online publication date: 1-Mar-2024
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Li JKang PSun WJiang Z(2024)Local residual preserving non-negative matrix factorization for multi-view clusteringNeurocomputing10.1016/j.neucom.2024.128054600:COnline publication date: 1-Oct-2024
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Liu GGe HLi TSu SGao P(2024)Robust multi-view clustering via collaborative constraints and multi-layer concept factorizationApplied Intelligence10.1007/s10489-024-05652-254:19(9446-9463)Online publication date: 1-Oct-2024
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Sun XZhu RYang MZhang XTang Y(2023)Sliced Sparse Gradient Induced Multi-View Subspace Clustering via Tensorial Arctangent Rank MinimizationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.318512635:7(7483-7496)Online publication date: 1-Jul-2023
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