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Multi-view dimensionality reduction learning with hierarchical sparse feature selection

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Abstract

Multi-view data can depict samples from various views and learners can benefit from such complementary information, so it has attracted extensive studies in recent years. However, it always locates in high-dimensional space and brings noisy or redundant views and features into the learning process, which can decrease the performance of the learner. To address the above issue, we propose a novel unsupervised Multi-view Dimensionality Reduction learning framework with Hierarchical Sparse Feature Selection (MvDRHSFS) to learn a low-dimensional subspace by jointly selecting the most informative views and features hierarchically. More specifically, we penalize the projection matrix with Frobenius norm (F-norm) and l2,1-norm to select the most informative views and features hierarchically. Under the penalty of the two regularization terms, some projection-based Sigle-view Dimensionality Reduction (SvDR) methods can learn a more meaningful low-dimensional subspace of multi-view data. In practical implementation, we use the regression type of PCA and relax the orthogonal constraint of the projection matrix to learn the low-dimensional subspace in a more flexible way. To find the optimal solution of the proposed learning framework, we derive an effective way to optimize the given formulation and give the theoretical analysis about the convergence for the optimization algorithm. Extensive experiment results on several real-world datasets demonstrate the feasibility and superiority of our proposed learning framework.

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  1. http://research.microsoft.com/en-us/projects/objectclassrecognition/

  2. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

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Acknowledgements

This work is supported by Natural Science Foundation of China under Grant No. 62076094, Shanghai Science and Technology Program “Distributed and generative few-shot algorithm and theory research” under Grant No. 20511100600, Shanghai Science and Technology Program “Federated based cross-domain and cross-task incremental learning” under Grant No. 21511100800, Chinese Defense Program of Science and Technology under Grant No. 2021-JCJQ-JJ-0041, China Aerospace Science and Technology Corporation Industry-University-Research Cooperation Foundation of the Eighth Research Institute under Grant No. SAST2021-007, and National Science Foundation of China for Distinguished Young Scholars under Grant No. 61725301.

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Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

    Wei Guo, Zhe Wang & Wenli Du

  2. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Wei Guo, Zhe Wang & Hai Yang

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  1. Wei Guo
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Correspondence to Zhe Wang.

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Guo, W., Wang, Z., Yang, H. et al. Multi-view dimensionality reduction learning with hierarchical sparse feature selection. Appl Intell 53, 12774–12791 (2023). https://doi.org/10.1007/s10489-022-04161-4

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