Title of article
Sparse, Robust and Discriminative Representation by Supervised Regularized Auto-Encoder
Author/Authors
Farajian, Nima Department of Computer Engineering - Faculty of Computer and Electrical Engineering University of Kashan Kashan, Iran , Adibi, Peyman Artificial Intelligence Department - Computer Engineering Faculty University of Isfahan Isfahan, Iran
Pages
9
From page
29
To page
37
Abstract
Recent researches have determined that regularized auto-encoders can provide a good representation of data
which improves the performance of data classification. These type of auto-encoders provides a representation of data
that has some degree of sparsity and is robust against variation of data to extract useful information and reveal the
underlying structure of data. The present study aimed to propose a novel approach to generate sparse, robust, and
discriminative features through supervised regularized auto-encoders, in which unlike most existing auto-encoders, the
data labels are used during feature extraction to improve discrimination of the representation and also, the sparsity
ratio of the representation is completely adaptive with data distribution. Results reveal that this method has better
performance in comparison to other regularized auto-encoders regarding data classification.
Keywords
Manifold , Discriminative Representation , Feature Learning , Supervised Auto-encoder , component
Journal title
International Journal of Information and Communication Technology Research
Serial Year
2019
Record number
2502267
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