The method of deep sparse auto-encoder networks has achieved state-of-art performance in the fields of image processing and natural language processing.It's been proved that higher accuracy of deep sparse auto-encoder networks is obtained by the increase of features' number

however

it also leads to a longer training time.In this paper

an optimized sparse auto-encoder networks which based on feature clustering is been presented for both classification accuracy enhancement and training time decreasing.The proposed method first get the number of substantive features by optimizing k-means clustering.Then initialize the network with that number and obtain the substantive features by training again the network.Finally the improvement of feature varieties is achieved by rotation and distortion of the substantive features.In the experiments

the improvement of classification accuracy and reduction of training time is verified by comparing the performance of optimized sparse auto-encoder with normal sparse auto-encoder in the basic dataset MNIST and CMU-PIE.