ML Anthology

Computing the Karhunen-Loeve Expansion with a Parallel, Unsupervised Filter System

Reiner Lenz, Mats Österberg
NeCo 1992 pp. 382-392
doi:10.1162/NECO.1992.4.3.382 /neco/1992/lenz1992neco-computing/

Abstract

We use the invariance principle and the principles of maximum information extraction and maximum signal concentration to design a parallel, linear filter system that learns the Karhunen-Loeve expansion of a process from examples. In this paper we prove that the learning rule based on these principles forces the system into stable states that are pure eigenfunctions of the input process.

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Cite

Text

Lenz and Österberg. "Computing the Karhunen-Loeve Expansion with a Parallel, Unsupervised Filter System." Neural Computation, 1992. doi:10.1162/NECO.1992.4.3.382

Markdown

[Lenz and Österberg. "Computing the Karhunen-Loeve Expansion with a Parallel, Unsupervised Filter System." Neural Computation, 1992.](https://mlanthology.org/neco/1992/lenz1992neco-computing/) doi:10.1162/NECO.1992.4.3.382

BibTeX

@article{lenz1992neco-computing,
  title     = {{Computing the Karhunen-Loeve Expansion with a Parallel, Unsupervised Filter System}},
  author    = {Lenz, Reiner and Österberg, Mats},
  journal   = {Neural Computation},
  year      = {1992},
  pages     = {382-392},
  doi       = {10.1162/NECO.1992.4.3.382},
  volume    = {4},
  url       = {https://mlanthology.org/neco/1992/lenz1992neco-computing/}
}