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An Incremental Autoencoder Approach for Data Stream Feature Extraction

Autoencoder is a neural network model which extracts the best representing features of data by performing unsupervised prelearning. Existing autoencoder approaches have been developed for applications that can access all data set at a time. However, the existing autocoders fail to satisfy for data stream applications that does not have access to the entire data set at once and and requires instant response. Therefore, this study presents an online sequential autocoder approach for data stream. The proposed autoencoder approach is based on the Extreme Learning Machines Autoencoder (ELM-AE) and adapts ELM-AE to be incremental learning algorithm. Therefore, Online Sequential-ELM (OS-ELM) algorithm which is incremental learning algorithm for data stream is integrated into ELM-AE in this paper. The proposed approach is tested on KddCup99, Poker, Pendigits, Hyperplane and ElecNormNews data stream sets which are commonly used in literature and achieved results demonstrate proposed approach's success.