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Erschienen in:
21.03.2015 | Original Article
Unsupervised extreme learning machine with representational features
Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2017
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Abstract
Extreme learning machine (ELM) is not only an effective classifier but also a useful cluster. Unsupervised extreme learning machine (US-ELM) gives favorable performance compared to state-of-the-art clustering algorithms. Extreme learning machine as an auto encoder (ELM-AE) can obtain principal components which represent original samples. The proposed unsupervised extreme learning machine based on embedded features of ELM-AE (US-EF-ELM) algorithm applies ELM-AE to US-ELM. US-EF-ELM regards embedded features of ELM-AE as the outputs of US-ELM hidden layer, and uses US-ELM to obtain the embedded matrix of US-ELM. US-EF-ELM can handle the multi-cluster clustering. The learning capability and computational efficiency of US-EF-ELM are as same as US-ELM. By experiments on UCI data sets, we compared US-EF-ELM k-means algorithm with k-means algorithm, spectral clustering algorithm, and US-ELM k-means algorithm in accuracy and efficiency.