On Maximum Entropy Linear Feature Inversion
Machine Learning
2024-07-22 v1
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.
Cite
@article{arxiv.2407.14166,
title = {On Maximum Entropy Linear Feature Inversion},
author = {Paul M Baggenstoss},
journal= {arXiv preprint arXiv:2407.14166},
year = {2024}
}
Comments
Submitted IEEE-Signal Processing Letters