Neural Networks with L-1 Regularizer for Sparse Representation of Input Data
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论文类型：会议论文
发表时间：2014-01-01
收录刊物：CPCI-S
页面范围：437-440
关键字：Neural network; Sparsification; L-1 regularization; Gradient descent method
摘要：A L-1 regularizer is proposed in this paper for the sparsification of the input data supplied to an artificial neural network. Gradient-descent method is used for solving the resulting optimization problem. Numerical experiments show the efficiency of the algorithm.