Graphs have become increasingly popular in modeling structures and interactions in a wide variety of problems during the last decade. Graph-based clustering and semi-supervised classification techniques have shown impressive performance. This paper proposes a graph learning framework to preserve both the local and global structure of data. Specifically, our method uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure. Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance. By considering rank constraint, the achieved graph will have exactly c connected components if there are c clusters or classes. As a byproduct of this, graph learning and label inference are jointly and iteratively implemented in a principled way. Theoretically, we show that our model is equivalent to a combination of kernel k-means and k-means methods under certain condition. Extensive experiments on clustering and semi-supervised classification demonstrate that the proposed method outperforms other state-of-the-art methods.
|State||Published - Feb 2021|
Bibliographical noteFunding Information:
This paper was in part supported by Grants from the National Key R&D Program of China (No. 2018YFC0807500 ), the Natural Science Foundation of China (Nos. 61806045 , U19A2059 ), the Sichuan Science and Techology Program under Project 2020YFS0057, the Ministry of Science and Technology of Sichuan Province Program (Nos. 2018GZDZX0048, 20ZDYF0343), the Fundamental Research Fund for the Central Universities under Project ZYGX2019Z015 .
© 2020 Elsevier Ltd
- Kernel method
- Local ang global structure
- Rank constraint
- Semi-supervised classification
- Similarity graph
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence