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Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data | IEEE Journals & Magazine | IEEE Xplore

Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data


Abstract:

Multi-view data can represent objects from different perspectives and thus provide complementary information for data analysis. A topic of great importance in multi-view ...Show More

Abstract:

Multi-view data can represent objects from different perspectives and thus provide complementary information for data analysis. A topic of great importance in multi-view learning is to locate a low-dimensional latent subspace, where common semantic features are shared by multiple data sets. However, most existing methods ignore uncorrelated items (i.e., view-specific features) and may cause semantic bias during the process of common feature learning. In this article, we propose a non-negative correlated and uncorrelated feature co-learning (CoUFC) method to address this concern. More specifically, view-specific (uncorrelated) features are identified for each view when learning the common (correlated) feature across views in the latent semantic subspace. By eliminating the effects of uncorrelated information, useful inter-view feature correlations can be captured. We design a new objective function in CoUFC and derive an optimization approach to solve the objective with the analysis on its convergence. Experiments on real-world sensor, image, and text data sets demonstrate that the proposed method outperforms the state-of-the-art multiview learning methods.
Page(s): 1486 - 1496
Date of Publication: 29 April 2020

ISSN Information:

PubMed ID: 32356763

Funding Agency:


References

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