Feature Extraction by Distance Neural Network in Classification Tasks

摘要

Feature extraction is an important task for data mining applications, in particular for classification. On one hand it leads to a better understanding of the relations between features and classification results. On the other hand it helps to perform fast classifications with a reduced number of features. Principal component analysis (PCA) is one of the mostly used techniques for linear feature extraction. Recently neural networks have been proposed for non-linear feature extraction outperforming PCA in many cases like non-linear principal component analysis (NLPCA). The mentioned approaches have in common an error reduction when reproducing the initial feature space from the reduced space. We present a new approach which tries instead conserving the patterns distribution from the original space to the reduced space. This model called dNN (Distance Neural Network) provides very good results for several cases outperforming PCA and shows to be competitive with the best non-linear techniques for feature extraction.