Two-stage Unsupervised Multiple Kernel Extreme Learning Machine

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Conference Proceeding
2018 International Joint Conference on Neural Networks (IJCNN), 2018
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10.1109@ijcnn.2018.8489529.pdfPublished version2.5 MB
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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.
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