Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.
In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.
College of Information and Electrical Engineering, Ludong University, Yantai 264025, China.
Shan
0000-0003-2956-1197
S
Pang
Department of Aircraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China.
Xinyi
X
Yang
D000465:Algorithms; D006236:Handwriting; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer