16 December 1992 Dimensionality reduction for nonlinear time series
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Abstract
A technique for recoding multidimensional data in a representation of reduced dimensionality is presented. A non-linear encoder-decoder for multidimensional data with compact representations is developed. The technique of training a neural network to learn the identity map through a `bottleneck' is extended to networks with non-linear representations, and an objective function which penalizes entropy of the hidden unit activations is shown to result in low dimensional encodings. For scalar time series data, a common technique is phase-space reconstruction by embedding the time-lagged scalar signal in a higher dimensional space. Choosing the proper embedding dimension is difficult. By using non-linear dimensionality reduction, the intrinsic dimensionality of the underlying system may be estimated.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David DeMers, David DeMers, } "Dimensionality reduction for nonlinear time series", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130829; https://doi.org/10.1117/12.130829
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