A Generative Model for Sparse Hyperparameter Determination
Zhiqiang Wan, Haibo He, Bo Tang · IEEE Transactions on Big Data · 2017
Sparse autoencoder is an unsupervised feature extractor and has been widely used in the machine learning and data mining community. However, a sparse hyperparameter has to be determined to balance the trade-off between the reconstruction error and the sparsity of sparse autoencoder. Traditional sparse hyperparameter determination method is time-consuming, especially when the dataset is large. In this paper, we derive a generative model for sparse autoencoder. Based on this model, we derive a formulation to determine the sparse hyperparameter effectively and efficiently. The relationship between the sparse hyperparameter and the average activation of sparse autoencoder hidden units is also presented in this paper. Experimental results and comparative studies over numerous datasets demonstrate the effectiveness of our method to determine the sparse hyperparameter.