Abstract

This paper investigates the feasibility of using artificial neural networks as a tool for data compression. More precisely, the paper measures compression capabilities of the standard multilayer perceptrons. An outline of a possible ``neural'' data compression method is given. The method is based on training a perceptron to reproduce a given data file. Experiments are presented, where the outlined method has been simulated by using differently configured perceptrons and various data files. The best compression rates obtained in the experiments are listed, and compared with similar results produced in a previous paper by holographic neural networks.

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Cite this article

@article{cai-1622,
  author  = {Robert Manger and Krunoslav Puljić},
  title   = {Multilayer Perceptrons and Data Compression},
  journal = {Computing and Informatics},
  volume  = {26},
  number  = {1},
  pages   = {45--62},
  year    = {2007},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2007/1/multilayer-perceptrons-and-data-compression/}
}
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