Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification

By Feiping Nie, Guohao Cai, Jing Li, Xuelong Li. IEEE Transactions on Image Processing, 2018

372 citations recorded by the source corpus.

Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance. Generally, in the field of multi-view learning, these algorithms construct informative graph for each view, on which the following clustering or classification procedure are based. However, in many real-world data sets, original data always contain noises and outlying entries that result in unreliable and inaccurate graphs, which cannot be ameliorated in the previous methods. In this paper, we propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously. The obtained optimal graph can be partitioned into specific clusters directly. Moreover, our model can allocate ideal weight for each view automatically without explicit weight definition and penalty parameters. An efficient algorithm is proposed to optimize this model. Extensive experimental results on different real-world data sets show that the proposed model outperforms other state-of-the-art multi-view algorithms.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/117f753ea6f7270a021af6e83a54f12778d0eb20

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Bing Liu
2. Xiaoqiang Yan
3. Hui Yu
4. Yangdong Ye
5. Menglei Hu
6. Shizhe Hu
7. Hao Wang
8. Songcan Chen

In Media Technology

1. Bing Liu
2. Hao Wang
3. Songcan Chen
4. Yan Yang
5. Zhao Kang
6. Erlin Pan

Citations in the corpus

• Doubly Aligned Incomplete Multi-view Clustering (2018, background): “Nowadays, multi-view learning has been widely studied in different areas such as face recognition, image processing and natural language processing [Romero et al. , 2017; Xing et al. , 2017; Nie et al. , 2018 ] .”
• GMC: Graph-Based Multi-View Clustering (2020, methodology): “Although these methods generate the final clusters with no additional clustering algorithms, they construct the graph of each view in isolation and keep the constructed graph fixed during fusion except [8] and [11], which only learn a global graph for all views (without building a graph for each vie”
• Deep multi-view learning methods: A review (2021, methodology): “The traditional MF-based MVL approaches can be partitioned into two types: multiple kernel/graph [114, 115, 116, 117] and subspace learning methods [118, 119].”