Aiming at equilibrium problem between the effectiveness and time of feature learning

a fast and efficient feature learning method is proposed. A new feature extraction model is combined with sparse autoencoder and softmax regression. After extracting the potential information of original image

the model take advantage of return value of multiple classifier which can respond the similarity of input information

learning feature vector for classification fastly and efficiently. In view of the label information is known

this algorithm is better than several typical feature learning methods in the image classification. In order to make the proposed algorithm has better generalization ability

add L2-norm into loss function of regression model to prevent overfitting. At the same time

use stochastic gradient descent method to get the optimal parameters of the model. The test results on four standard data sets show that the proposed algorithm is feasible and effective.