R. Saegusa and S. Hashimoto (Japan)
nonlinear PCA, MLP, dimensionality reduction
Principal Component Analysis (PCA) is a useful method in multivariate analysis to reduce the dimen sionality of data. We have already proposed a non linearly extended model of PCA by employing neural networks and have shown its effectiveness with some artificial data. In this paper, we report results of a nonlinear principal component analysis on real-world data utilizing the proposed method. Moreover, we compare the distribution of reconstructed data with the distribution of the original data to discuss the ad vantage of nonlinear PCA.
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