ELM based multiple kernel k-means with diversity-induced regularization

Yang Zhao, Y. Dou, Xinwang Liu, Teng Li
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引用次数: 1

Abstract

Multiple-kernel k-means (MKKM) clustering has demonstrated good clustering performance by combining pre-specified kernels. In this paper, we argue that deep relationships within data and the complementary information among them can improve the performance of MKKM. To illustrate this idea, we propose a diversity-induced MKKM algorithm with extreme learning machine (ELM)-based feature extracting method. First, ELM, which has randomly chosen weights of hidden and output nodes, is applied to thoroughly extract features from data by generating different numbers of hidden nodes and using different functions. Second, an MKKM algorithm with diversity-induced regularization is utilized to explore the complementary information among kernels constructed from features. The problem could be solved efficiently by alternating optimization. Experimental results demonstrate that the proposed method outperforms state-of-the-art kernel methods.
基于ELM的多核k-均值分集正则化算法
多核k-均值(MKKM)聚类通过组合预先指定的核,显示出良好的聚类性能。在本文中,我们认为数据之间的深层关系和它们之间的互补信息可以提高MKKM的性能。为了说明这一思想,我们提出了一种基于极限学习机(ELM)特征提取方法的多样性诱导MKKM算法。首先,利用随机选择隐藏节点和输出节点权重的ELM,通过生成不同数量的隐藏节点和使用不同的函数,从数据中彻底提取特征。其次,利用分集诱导正则化的MKKM算法,探索由特征构造的核之间的互补信息;交替优化可以有效地解决这一问题。实验结果表明,该方法优于目前最先进的核方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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