Journal Article10.1109/TNNLS.2015.2469673
A Multiobjective Sparse Feature Learning Model for Deep Neural Networks
205
TL;DR: This paper proposes a multiobjective sparse feature learning model based on the autoencoder that can learn useful sparse features and designs a multi objective induced learning procedure based on a multiObjective evolutionary algorithm.
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Abstract: Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.
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