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Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis

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Abstract

Multi-view data can collaborate with each other to provide more comprehensive information than single-view data. Although there exist a few unsupervised multi-view representation learning methods taking both the discrepancies and incorporating complementary information from different views into consideration, they always ignore the use of inner-view discriminant information. It remains challenging to learn a meaningful shared representation of multiple views. To overcome this difficulty, this paper proposes a novel unsupervised multi-view representation learning model, MRL. Unlike most state-of-art multi-view representation learning, which only can be used for clustering or classification task, our method explores the proximity guided representation from inner-view and complete the task of multi-label classification and clustering by the discrimination fusion representation simultaneously. MRL consists of three parts. The first part is a deep representation learning for each view and then aims to represent the latent specific discriminant characteristic of each view, the second part builds a proximity guided dynamic routing to preserve its inner features of direction,location and etc. At last, the third part, GCCA-based fusion, exploits the maximum correlations among multiple views based on Generalized Canonical Correlation Analysis (GCCA). To the best of our knowledge, the proposed MRL could be one of the first unsupervised multi-view representation learning models that work in proximity guided dynamic routing and GCCA modes. The proposed model MRL is tested on five multi-view datasets for two different tasks. In the task of multi-label classification, the results show that our model is superior to the state-of-the-art multi-view learning methods in precision, recall, F1 and accuracy. In clustering task, its performance is better than the latest related popular algorithms. And the performance varies w.r.t. the dimensionality of G is also made to explore the characteristics of MRL.

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Acknowledgements

Partially supported by the National Natural Science Foundation of China (Grant No. 61872260),Key Research and Development Program International Cooperation Projectof Shanxi Province of China(Grant No.201703D421013), Institute Level Research Fund Project of Shanxi Energy College (Grant No.ZY-2017002),Institute Level Research Fund Project of Shanxi Energy College (Grant No.SY-2018004).

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Authors and Affiliations

  1. College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, People’s Republic of China

    Tingyi Zheng

  2. Department of Electrical and Power Engineering, Shanxi Institute of Energy, Taiyuan, People’s Republic of China

    Tingyi Zheng

  3. College of Mathematics, Taiyuan University of Technology, Taiyuan, People’s Republic of China

    Huibin Ge

  4. Geovis Technology Co., Ltd, Beijing, China

    Jiayi Li

  5. College of Data Science, Taiyuan University of Technology, Taiyuan, People’s Republic of China

    Li Wang

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  1. Tingyi Zheng
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Correspondence to Li Wang.

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The authors take the position that the data mining based on AI technologies are meaningful for social applications and people life. In the presented paper, they research on the multi-view data mining and design a fusion representation learning method of multi-view data. Through the paper, the authors aim to present a brief description of model and explain how such model might be used to improve the multi-view fusion learning ability. The authors also plan to design more high-level multi-view representation leaning method through future studies and analyses. It is hoped that the preliminary findings from these follow-up studies will be ready for presentation.

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Zheng, T., Ge, H., Li, J. et al. Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis. Appl Intell 51, 248–264 (2021). https://doi.org/10.1007/s10489-020-01821-1

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