Abstract
Fuzzy c-Means-based Classifier (FCMC) has been proved to have high performances based on clustering concepts in conjunction with several parameter optimization methods. In general, FCMC is applied to high-dimensional data after dimension reduction by Principal Component Analysis (PCA). In this paper, the applicability of Independent Component Analysis (ICA)-based dimension reduction is investigated in the FCMC context. ICA is a computational method for separating a multivariate signal into additive subcomponents with the assumption of non-Gaussian signals. This paper compares the performance of FCMC using four data sets. Two initialization approaches of the PCA-Tree-based and k-dimensional tree (kd-Tree)-based are also compared.
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Kobayashi, T., Honda, K., Notsu, A., Ichihashi, H. (2013). Applicability of ICA-Based Dimension Reduction in Fuzzy c-Means-Based Classifier. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_13
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DOI: https://doi.org/10.1007/978-3-642-42051-1_13
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