Subspace clustering algorithms have been found successful in various applications that involve two-dimensional data, i.e., each example of the data is a matrix. However, most of the existing methods transform the matrix-type examples to vectors in a pre-processing step, which omits and severely damages the inherent structural information of such data. In this paper, we propose a novel subspace clustering method for two-dimensional data, which is capable of extracting the most representative structural information from the data to recover the underlying grouping relationships of the data. The structural features are extracted from two views of the data and the numbers of feature spaces in both views are automatically determined by optimization. Extensive experiments confirm the effectiveness of the proposed method.
|State||Published - Dec 22 2022|
Bibliographical noteFunding Information:
This work was supported by in part by the National Natural Science Foundation of China (NSFC) under Grants 62172246 , 62106063 , 61802215 , and 61806045 ; in part by the Natural Science Foundation of Shandong Province under Grants ZR2019QF009 and ZR2019BF011 ; in part by the Guangdong Natural Science Foundation under Grant 2022A1515010819 ; in part by the Shenzhen College Stability Support Plan under Grant GXWD20201230155427003-20200824113231001 ; in part by the Shandong Province Colleges and Universities Youth Innovation Technology Plan Innovation Team Project under Grant 2021KJ062 and Grant 2020KJN011 ; in part by the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under Grant 2022B1212010005 ; and in part by the National Institutes of Health (NIH) under Grants R21AG070909 and UH3 NS100606-03 .
© 2022 Elsevier B.V.
- Ridge regression
- Structural information
- Subspace clustering
- Two-dimensional data
ASJC Scopus subject areas
- Management Information Systems
- Information Systems and Management
- Artificial Intelligence