Work

On Maximum Entropy Linear Feature Inversion

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2407.14166

Author: Baggenstoss, Paul M

Institution: Fraunhofer Institute for Communication, Information Processing and Ergonomics

Language: English

Topic: Numerical methods in inverse problems

Subfield: Mathematical Physics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4406055023

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