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Exploiting label information to improve auto-encoding based classifiers
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DOI：10.1016/j.neucom.2019.08.055.png)
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Abstract
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In this paper, we propose to provide more information to Stacked Denoising Auto-Encoding classifiers in order to increase their performance. Specifically, we use the output of an auxiliary classifier to extend the input to those machines, and carry out the layer-by-layer auto-encoding training considering the input recovering and the label errors by means of a convex combination whose parameter is selected by conventional cross-validation. Extensive experiments support the effectiveness of this proposal, showing that the resulting machines offer better results than standard designs in all the cases, as well as a reduced sensitivity to the design parameters. The main conclusion of this study plus a number of avenues for further research close this contribution. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Auto-encoding
Classification
Convex combination
Depth
Label error
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