A Multiobjective Sparse Feature Learning Model for Deep Neural Networks

IEEE Transactions on Neural Networks and Learning Systems
Maoguo GongLinzhi Su

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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Citations

Aug 31, 2010·Current Opinion in Neurobiology·Viren JainSrinivas C Turaga
Feb 18, 2017·IEEE Transactions on Neural Networks and Learning Systems·Zhengshan Dong, Wenxing Zhu
May 11, 2017·IEEE Transactions on Neural Networks and Learning Systems·Jia LiuHao Li
Jul 7, 2017·IEEE Transactions on Neural Networks and Learning Systems·Wei LuoJian Zhang
Jun 24, 2017·IEEE Transactions on Neural Networks and Learning Systems·Yue-Jiao GongYicong Zhou
May 16, 2017·IEEE Transactions on Neural Networks and Learning Systems·Xiaoheng JiangXuelong Li
Aug 18, 2017·IEEE Transactions on Neural Networks and Learning Systems·Oyebade K Oyedotun, Adnan Khashman
Jul 11, 2018·IEEE Transactions on Neural Networks and Learning Systems· Fuyong Xing Lin Yang
Feb 20, 2019·Neural Networks : the Official Journal of the International Neural Network Society·Jia LiuHaibo He

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