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- W792551570 abstract "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." @default.
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- W792551570 date "2015-10-01" @default.
- W792551570 modified "2023-10-16" @default.
- W792551570 title "Discriminative clustering via extreme learning machine" @default.
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- W792551570 doi "https://doi.org/10.1016/j.neunet.2015.06.002" @default.
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