A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders

TitleA First Approach to Face Dimensionality Reduction Through Denoising Autoencoders
Publication TypeConference Paper
Year of Publication2018
AuthorsPulgar-Rubio, F., Charte Francisco, Rivera Antonio J., and del Jesus M. J.
Conference Name19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Pagination439–447
Date Published11
Conference LocationMadrid (Spain)
ISBN Number978-3-030-03493-1
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.

Notes

TIN2015-68854-R; FPU16/00324

DOI10.1007/978-3-030-03493-1_46