期刊
IEEE ACCESS
卷 7, 期 -, 页码 42639-42651出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2907043
关键词
Dimension reduction; feature selection; K-means; discriminative embedded clustering; adaptive learning
资金
- National Natural Science Foundation of China, China [61871464]
- National Natural Science Foundation of Fujian Province, China [2017J01511]
- Scientific Research Fund of Fujian Provincial Education Department [JAT170417, JAT160357]
- Ministry of Science and Technology, Taiwan [MOST-104-2221-E-324-019-MY2, MOST-106-2221-E-324-025, MOST-107-2221-E-324-018-MY2]
- Climbing'' Program of Xiamen University of Technology [XPDKQ18012]
In many real-world applications, data are represented by high-dimensional features. Despite the simplicity, existing K-means subspace clustering algorithms often employ eigenvalue decomposition to generate an approximate solution, which makes the model less efficiency. Besides, their loss functions are either sensitive to outliers or small loss errors. In this paper, we propose a fast adaptive K-means (FAKM) type subspace clustering model, where an adaptive loss function is designed to provide a flexible cluster indicator calculation mechanism, thereby suitable for datasets under different distributions. To find the optimal feature subset, FAKM performs clustering and feature selection simultaneously without the eigenvalue decomposition, therefore efficient for real-world applications. We exploit an efficient alternative optimization algorithm to solve the proposed model, together with theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on several benchmark datasets demonstrate the advantages of FAKM compared to state-of-the-art clustering algorithms.
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