![Illustration of proximity guided dynamic routing. Observations O1, …, OJ correspond to the mean-centered output of the first network, and Oj∗j=1J\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\left\{{O}_j^{\ast}\right\}}_{j=1}^J $$\end{document} contains the different views totally projected by proximity guided dynamic routing. Sk2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {S}_{k_2} $$\end{document} is the k2th capsule. The initial coupling coefficients are then iteratively refined by measuring the agreement between the current output Vk2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {V}_{k_2} $$\end{document} of each capsule in the above layer](https://www.researchgate.net/publication/343566502/figure/fig5/AS:960496178044929@1606011542905/Illustration-of-proximity-guided-dynamic-routing-Observations-O1-OJ-correspond-to_Q320.jpg)
January 2021
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85 Reads
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8 Citations
Applied Intelligence
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.