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Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints.

IEEE Transactions on Neural Networks and Learning Systems, no. 12 (2016): 2486-2498

Cited by: 152|Views65
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Abstract

We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (nonnegativity-constrained autoencoder), that learns features that show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposi...More

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