Incremental Feature Construction for Deep Learning Using Sparse Auto-Encoder

Abstract

A sparse auto-encoder is one of effective algorithms for learning features from unlabeled data in deep neural-network learning. In conventional sparse auto-encoder training, for each layer of a deep neuralnetwork, all feature units are simultaneously constructed at the beginning and after being trained, several similar/ redundant features are obtained at… (More)

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