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Nonlinear Dimensionality Reduction by Multi Layer Perceptron Using Superposed Energy[edit]

T. Takahashi, and R. Tokunaga[edit]

Proceedings of 1999 International Symposium on Nonlinear Theory and its Applications(NOLTA99), vol. 2, pp. 863--866, 1999.

Abstract[edit]

We investigate an energy function for MLP called superposed energy. Applying to autoassociative learning of a sandglass-type MLP, it can adaptively adjust the effective number of the bottleneck-layer units to the intrinsic dimensionality of nonlinear data, and the optimal dimensionality reduced representation can be extracted after learning.


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Last-modified: 2014-08-13 (水) 13:45:19 (1139d)