Mixtures of probabilistic principal component analyzers
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The adaptation of a neural gas algorithm to local principal component analysis (NG-LPCA) is a useful technique in data compression, pattern recognition, classification or even in data estimation. However, the batch NG-LPCA becomes unfeasible when dealing with high dimensional data. In this paper, a regularization method is described in detail to prevent the batch NG-LPCA approach from instability. The proposed method is tested and the results seem to prove that it is a suitable tool for classifying tasks avoiding instability with high dimensional datasets.