Multi-view clustering: A survey

By Yan Yang, Hao Wang. Big Data Mining and Analytics, 2018

436 citations recorded by the source corpus.

In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9404cc4488ae98babf17b286a20e7baa6ef5d398

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bing Liu
2. Hao Wang
3. Yan Yang
4. Zixuan Ke
5. Hu Xu
6. Lei Shu
7. B. Liu
8. Sahisnu Mazumder

In Computer Vision and Pattern Recognition

1. Bing Liu
2. Hao Wang
3. Yan Yang
4. Zixuan Ke
5. Sahisnu Mazumder
6. Hu Xu
7. Zhao Kang
8. Erlin Pan

In Statistical and Nonlinear Physics

1. Bing Liu

Citations in the corpus

• GMC: Graph-Based Multi-View Clustering (2020, background): “Multi-view clustering explores and exploits the complementary information from multiple views to produce a more accurate and robust partitioning of the data than single-view clustering [3], [4].”