Abstract
Here, we propose a fast deep learning architecture for feature representation. The target of deep learning in our model is to capture the relevant higher-level abstraction from disentangling input features, which is possible due to the speed of the extreme learning machine (ELM). We use ELM auto encoder (ELM-AE) to add a regularization term into ELM for improving generalization performance. To demonstrate our model with a high performance for deep representation, we conduct experiments on the MNIST database and compare the proposed method with state-of-the-art deep representation methods. Experimental results show the proposed method is competitive for deep representation and reduces amount of time needed for training.
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