A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders

Comunicación en congreso 2018

Franciso Javier Pulgar Rubio, Francisco Charte Ojeda, Antonio Jesús Rivera Rivas, Maria José del Jesus Díaz

Páginas 439-447

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Cómo citar esta publicación

Pulgar Rubio, F. J., Charte Ojeda, F., Rivera Rivas, A. J., & del Jesus Díaz, M. J. (2018). A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders. 439-447. Madrid (Spain). https://doi.org/10.1007/978-3-030-03493-1_46
BibTeX

Resumen

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.

Financiación

  • TIN2015-68854-R
  • FPU16/00324

Detalles

DOI
10.1007/978-3-030-03493-1_46
Date Published
11
Lugar del congreso
Madrid (Spain)
ISBN
978-3-030-03493-1