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Proceedings of the Fifth Internatinal Conference on Neural Information Processing (ICONIP'98), vol.1, pp.494-497, 1998.
This paper investigates two simple energy functions which are valid for two different purposes of dimensionality reduction: feature extraction and data compression. These energy functions enable nonlinear perceptrons to organize data representations whose parameters, namely, outputs of the bottleneck layer units, are arranged in the order of their importance. The efficacy of these energy functions is shown by numerical experiments in comparison with conventional squared error functions and Principal Component Analysis.
