Training auto-encoders effectively via eliminating task-irrelevant input variables Online publication date: Mon, 15-Apr-2019
by Hui Shen; Dehua Li; Hong Wu; Zhaoxiang Zang
International Journal of Computational Science and Engineering (IJCSE), Vol. 18, No. 4, 2019
Abstract: Auto-encoders are often used as building blocks of deep network classifiers to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalisation performance of the network. In this paper, via dropping the task-irrelevant input variables, the performance of auto-encoders can be obviously improved. Specifically, an importance-based variable selection method is proposed to aim at finding the task-irrelevant input variables and dropping them. It firstly estimates importance of each variable, and then drops the variables with importance value lower than a threshold. In order to obtain better performance, the method can be employed for each layer of stacked auto-encoders. Experimental results show that when combined with our method the stacked denoising auto-encoders achieve significantly improved performance on three challenging datasets.
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