Local Invariance Representation Learning Algorithm with Multi-layer Extreme Learning Machine
Multi-layer extreme learning machine (ML-ELM) is a stacked extreme learning machine based auto-encoding (ELM-AE). It provides an effective solution for deep feature extraction with higher training efficiency. To enhance the local-input invariance of feature extraction, we propose a contractive multi-layer extreme learning machine (C-ML-ELM) by adding a penalty term in the optimization function to minimize derivative of output to input at each hidden layer. In this way, the extracted feature is supposed to keep consecutiveness attribution of an image. The experiments have been done on MNIST handwriting dataset and face expression dataset CAFÉ. The results show that it outperforms several state-of-art classification algorithms with less error and higher training efficiency.
KeywordsLocal invariant representation learning Multi-layer extreme learning Contractive auto-encoder
This research is partially sponsored by the National Nature Science Foundation of China (Nos. 61672070, 61370113, 91546111), Beijing Municipal Natural Science Foundation (No. 4152005), Key projects of Beijing Municipal Education Commission (No. KZ201610005009).
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