Exploring autoencoders for unsupervised feature selection

Feature selection plays an important role in pattern classification. It is especially an important preprocessing task when there are large number of features in comparison to number of patterns as is the case with gene expression data. A new unsupervised feature selection method has been evolved using autoencoders since autoencoders have the capacity to learn the input features without class information. In order to prevent the autoencoder from overtraining, masking has been used and the reconstruction error of masked input features has been used to compute feature weights in moving average manner. A new aggregation function for autoencoder has also been introduced by incorporating correlation between input features to remove the redundancy in selected features set. Comparative performance evaluation on benchmark image and gene expression datasets shows that the proposed method outperforms other unsupervised feature selection methods.