期刊
NEURAL NETWORKS
卷 70, 期 -, 页码 1-8出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2015.06.002
关键词
Discriminative clustering; Extreme learning machine; k-means; Linear discriminant analysis
资金
- National Natural Science Foundation of China [41427806, 61273233]
- Research Fund for the Doctoral Program of Higher Education [20120002110035, 20130002130010]
- Project of China Ocean Association [DY125-25-02]
- Tsinghua University Initiative Scientific Research Program [20131089300]
- Singapore Academic Research Fund (AcRF) Tier 1 [RG 80/12 (M4011092)]
- National Science Foundation of China [61101216]
- 111 Project [111-2-05]
Discriminative clustering is an unsupervised learning framework which introduces the discriminative learning rule of supervised classification into clustering. The underlying assumption is that a good partition (clustering) of the data should yield high discrimination, namely, the partitioned data can be easily classified by some classification algorithms. In this paper, we propose three discriminative clustering approaches based on Extreme Learning Machine (ELM). The first algorithm iteratively trains weighted ELM (W-ELM) classifier to gradually maximize the data discrimination. The second and third methods are both built on Fisher's Linear Discriminant Analysis (LDA); but one approach adopts alternative optimization, while the other leverages kernel k-means. We show that the proposed algorithms can be easily implemented, and yield competitive clustering accuracy on real world data sets compared to state-of-the-art clustering methods. (C) 2015 Elsevier Ltd. All rights reserved.
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