A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders

Abstract

The problem of high dimensionality is a challenge when facing machine learning tasks. A high dimensional space has a negative effect on the predictive performance of many methods, specifically, classification algorithms. There are different proposals that arise to mitigate the effects of this phenomenon. In this sense, models based on deep learning have emerged. In this work, denoising autoencoders (DAEs) are used to reduce dimensionality. To verify its performance, an experimentation is carried out where the improvement obtained with different types of classifiers is verified. The classification method used are: kNN, SVM, C4.5 and MLP. The test for kNN and SVM show a better predictive performance for all datasets. The executions for C4.5 and MLP reflect improvements only in some cases. The execution time is lower for all tests. In addition, a comparison between DAEs and PCA, a classical method of dimensionality reduction, is performed, obtaining better results with DAEs in most cases. The conclusions reached open up new lines of future work.