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Deep learning algorithms for discriminant autoencoding
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DOI：10.1016/j.neucom.2017.05.042.png)
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Abstract
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In this paper, a new family of Autoencoders (AE) for dimensionality reduction as well as class discrimination is proposed, using various class separating methods which cause a translation of the reconstructed data in a way such that the classes are better separated. The result of this combination is a new type of Discriminant Autoencoder, in which the targets are shifted in space in a discriminative fashion. The proposed Discriminant AE is experimentally compared to the standard Denoising AE in the challenging classification tasks of handwritten digit recognition and facial expression recognition as well as in the CIFAR10 dataset. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Autoencoders
Data Separation
Dimensionality Reduction
Graph Embedding Framework
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