title: Discriminative clustering via extreme learning machine.
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.
authors.affiliation: Department of Automation, Tsinghua University, Beijing 100084, China. Electronic address: huang-g09@mails.tsinghua.edu.cn.
authors.forename: Gao
authors.initials: G
authors.lastname: Huang
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore. Electronic address: tcliu@ntu.edu.sg.
authors.forename: Tianchi
authors.initials: T
authors.lastname: Liu
authors.affiliation: Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, Nanyang Avenue, 639798, Singapore; State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China. Electronic address: y.yang@ntu.edu.sg.
authors.forename: Yan
authors.initials: Y
authors.lastname: Yang
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore. Electronic address: ezplin@ntu.edu.sg.
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Lin
authors.affiliation: Department of Automation, Tsinghua University, Beijing 100084, China. Electronic address: shijis@mail.tsinghua.edu.cn.
authors.forename: Shiji
authors.initials: S
authors.lastname: Song
authors.affiliation: Department of Automation, Tsinghua University, Beijing 100084, China. Electronic address: wuc@tsinghua.edu.cn.
authors.forename: Cheng
authors.initials: C
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016002:Discriminant Analysis; D016014:Linear Models; D016571:Neural Networks, Computer; D000069553:Supervised Machine Learning
keywords: -means; Discriminative clustering; Extreme learning machine; Linear discriminant analysis