Deep Multiple Auto-Encoder-Based Multi-view Clustering

• Published in: Data Science and Engineering Vol. 6; no. 3; pp. 323 - 338
• Main Authors: Du, Guowang, Zhou, Lihua, Yang, Yudi, Lü, Kevin, Wang, Lizhen
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.09.2021; Springer; Springer Nature B.V
• Subjects: Algorithm Analysis and Problem Complexity; Algorithms; Analysis; Artificial Intelligence; Chemistry and Earth Sciences; Clustering; Coders; Computer Science; Data Mining and Knowledge Discovery; Database Management; Physics; Representations; Statistics for Engineering; Systems and Data Security; Multi-view Clustering; Local geometrical information; Auto-encoder; Consistent information; Complementary information
• ISSN: 2364-1185; 2364-1541
• DOI: 10.1007/s41019-021-00159-z

Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information of different views, has brought along a growth of attention. Multi-view clustering algorithms based on different theories have been proposed and extended in various applications. However, most existing MVC algorithms are shallow models, which learn structure information of multi-view data by mapping multi-view data to low-dimensional representation space directly, ignoring the nonlinear structure information hidden in each view, and thus, the performance of multi-view clustering is weakened to a certain extent. In this paper, we propose a deep multi-view clustering algorithm based on multiple auto-encoder, termed MVC-MAE, to cluster multi-view data. MVC-MAE adopts auto-encoder to capture the nonlinear structure information of each view in a layer-wise manner and incorporate the local invariance within each view and consistent as well as complementary information between any two views together. Besides, we integrate the representation learning and clustering into a unified framework, such that two tasks can be jointly optimized. Extensive experiments on six real-world datasets demonstrate the promising performance of our algorithm compared with 15 baseline algorithms in terms of two evaluation metrics.