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On the contractive nature of autoencoders: application to missing sensor restoration

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3 Author(s)
Thompson, B.B. ; Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA ; Marks, R.J. ; El-Sharkawi, M.A.

The neural network autoencoder is a useful tool for the restoration of missing sensors when enough known sensors with some relation to those missing are available. Through the idea of a contraction mapping, this paper provides some insight into the convergence of several iterative methods of sensor restoration using the autoencoder to some unique answer given a specific operating point (i.e., the known sensor values), regardless of how the missing sensor values are initialized.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003