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Two-stage Unsupervised Multiple Kernel Extreme Learning Machine

机译：两阶段无监督多核极限学习机

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摘要

As a powerful learning tool, Extreme Learning Machine (ELM) shows its merits in classification, regression and clustering by offering both high prediction accuracy and high learning speed. Among numerous ELM varieties, multiple kernel ELM draws intensive attention from researchers because it can leverage information from multiple heterogeneous sources, which is a common scenario in big data era. Despite remarkable efforts for supervised multiple kernel ELM, few publications have addressed the unsupervised case, which is more critical yet challenging for tackling realistic problem. In this paper, we address this problem by proposing a two-stage unsupervised multiple kernel extreme learning machine, which is suitable for fast multiple-view clustering. This approach learns the cluster and kernel combination weights alternatively. At the first stage, it generates cluster label based on a given combined kernel. Then, at the second stage, the kernel combination weights are learned by distance label based extreme learning machine based on the label generated at the previous stage. Experimental results on both synthetic and real data sets demonstrate its outstanding performance in term of both accuracy and learning speed.
机译：作为一个强大的学习工具，极端学习机（ELM）通过提供高预测精度和高学习速度来显示分类，回归和聚类方面的优点。在众多榆树品种中，多个内核榆树从研究人员汲取强化关注，因为它可以利用来自多个异构来源的信息，这是大数据时代的常见情景。尽管对监督多个内核榆树有了显着努力，但很少有出版物已经解决了无人监督的情况，这对解决现实问题来说更为重要而挑战。在本文中，我们通过提出一个两阶段无监督的多个内核极端学习机来解决这个问题，这适用于快速多视图聚类。此方法可选地学习群集和内核组合权重。在第一阶段，它基于给定的组合内核生成群集标签。然后，在第二阶段，基于前阶段生成的标签，通过基于距离标签的极端学习机学习内核组合权重。合成和实际数据集的实验结果在准确性和学习速度方面展示了其出色的性能。

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