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Enhancing deep neural networks via multiple kernel learning
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DOI：10.1016/j.patcog.2020.107194.png)
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Abstract
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Deep neural networks and Multiple Kernel Learning are representation learning methodologies of widespread use and increasing success. While the former aims at learning representations through a hierarchy of features of increasing complexity, the latter provides a principled approach for the combination of base representations. In this paper, we introduce a general framework in which the internal representations computed by a deep neural network are optimally combined by means of Multiple Kernel Learning. The resulting ensemble methodology is instantiated for Multi-layer Perceptrons architectures (both fully trained and with random-weights), and for Convolutional Neural Networks. Experimental results on several benchmark datasets concretely show the advantages and potentialities of the proposed approach. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Deep neural networks
Deep learning
Multiple kernel learning
Ensemble learning
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