On Maximum Entropy Linear Feature Inversion
<|reference_start|>On Maximum Entropy Linear Feature Inversion: 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.<|reference_end|>
@article{baggenstoss2024on,
    title={On Maximum Entropy Linear Feature Inversion},
    author={Paul M Baggenstoss},
    journal={arXiv preprint arXiv:2407.14166},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14166},
    primaryClass={cs.LG}
}