Estimation of samples relevance by their histograms
M.A. Antonets
TL;DR
This paper discusses a method for estimating the relevance of samples based on their histograms using variational principles, including conditions for reducing the complexity of related linear programming problems.
Contribution
It introduces a novel approach for relevance estimation of samples through histogram analysis and provides conditions for simplifying associated linear programming tasks.
Findings
Relevance estimation can be effectively performed using histogram-based variational principles.
Conditions for dimension reduction of linear programming problems are identified.
The approach improves computational efficiency in relevance assessment.
Abstract
The problem of the estimation of relevance to a set of histograms generated by samples of a discrete time process is discussed on the base of the variational principles proposed in the previous paper [1]. Some conditions for dimension reduction of corresponding linear programming problems are presented also.
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Data Management and Algorithms