On Maximum Entropy Linear Feature Inversion

Paul M Baggenstoss
7/19/2024
cs.LG

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Abstract

We revisit the classical problem of inverting dimension-reducing linear mappings using the maximum entropy (MaxEnt) criterion. In the literature, solutions are problem-dependent, inconsistent, and use different entropy measures. We propose a new unified approach that not only specializes to the existing approaches, but offers solutions to new cases, such as when data values are constrained to [0, 1], which has new applications in machine learning.

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Cite this paper

@article{baggenstoss2024maximum,
  title  = {On Maximum Entropy Linear Feature Inversion},
  author = {Paul M Baggenstoss},
  year   = {2024},
  eprint = {2407.14166},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.14166v1}
}

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