Multi View Information Bottleneck Representation Learning
In real world applications, clustering or classification can usually be improved by fusing information from different views. Therefore, unsupervised representation learning on multi view data becomes a compelling topic in machine learning. In this paper, we propose a novel and flexible unsupervised multi view representation learning model termed Collaborative Multi View Information Bottleneck Networks (CMIB Nets) which comprehensively explores the common latent structure and the view specific intrinsic information, and discards the superfluous information in the data significantly improving the generalization capability of the model. Specifically, our proposed model relies on the information bottleneck principle to integrate the shared representation among different views and the view specific representation of each view, prompting the multi view complete representation and flexibly balancing the complementarity and consistency among multiple views. We conduct extensive experiments (including clustering analysis, robustness experiment, and ablation study) on real world datasets, which empirically show promising generalization ability and robustness compared to state of the arts. Introduction For real world applications, data are usually manifested in multiple types of features (Dhillon, Foster, and Ungar 2011) or multiple modalities (Ngiam et al. 2011; Baltrusaitis, Ahuja, and Morency 2018) that are considered as multiple views. For instance, an image can be described by color (e.g. color histogram (Novak, Shafer et al. 1992) or texture descriptor (e.g. GIST (Oliva and Torralba 2001) SIFT (Lowe 2004) HOG (Dalal and Triggs 2005) Basically, due to the diversity of feature extraction or data acquirement, various views are usually heterogeneous. The ubiquity of multi view data has attracted tremendous attention to the multi view representation learning (Sun 2013; Zhang et al. 2020) Furthermore, integrating different views into a compact representation is essential for the downstream specific tasks since the intact representation could be easily developed by off the shelf algorithms (Zhang et al. 2018; Liu et al. 2018) Generally, when information from different views complements each other, it can be expected that *Corresponding author Copyright c (c) 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org) All rights reserved. the multi view representation learning approaches can improve performance (Tao et al. 2019) To effectively explore the multi view data, a series of methods have been proposed in recent years (Wang et al. 2015) The representative ways are Canonical Correlation Analysis (CCA) (Hotelling 1992) and its variants, which mainly maximize the consistency of multiple views by projecting different views into a common subspace. However, the main drawback of the CCA based algorithms is that it overemphasizes exploring common information, while the view specific intrinsic information of each view is also important, which may degrade the quality of the learned representation. Consequently, it is still a long term challenge to jointly exploit the view specific information of each view and the complex relationships among different heterogeneous views under the context of multi view representation learning. In this work, to address the above issues, we propose an unsupervised multi view representation learning method termed Collaborative Multi View Information Bottleneck Networks (CMIB Nets) based on the information bottleneck principle. The proposed CMIB Nets aims to collaboratively encode and integrate the view specific intrinsic information and shared latent structure from heterogeneous views into a comprehensive representation. Moreover, the model can also adaptively balance the complementarity and consistency among different views. Specifically, we realize this by maximizing the mutual information between multi view information bottleneck representation and shared representation (learning the common structure) while at the same time maximizing the mutual information between multi view information bottleneck representation and view specific representations (learning the view specific information) We also minimize the mutual information between original views and multi view information bottleneck representation simultaneously (compressing data to remove useless information) The resulting multi view information bottleneck representation can integrate the advantages of other multi view representations, which not only encodes the view specific information and cross view underlying structure but also reduces the influence of superfluous information in the multi view data. Therefore, it can be more robust to downstream tasks and improve the generalization capability. Furthermore, by utilizing the deep neural networks, the obtained represenThe Thirty Fifth AAAI Conference on Artificial Intelligence (AAAI 21)
AAAI