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Jun 10, 2021 · In this work we present a simple approach based on concepts from statistical physics to learn optimal distance metric for a given problem.
Sep 11, 2024 · This provides a natural way to learn the distance metric, where the learning process can be intuitively seen as stretching and rotating the ...
Jun 10, 2021 · This work presents a simple approach based on concepts from statistical physics to learn optimal distance metric for a given problem and ...
The main objective of distance metric learning is to 'optimally' transform the data in order to bring similar points closer to each other while keeping the ...
Learning an appropriate distance metric from data is usually superior to the default Euclidean distance. In this paper, we revisit the original model proposed ...
We present a reformulation of the distance metric learning problem as a penalized optimization problem, with a penalty term corresponding to the von Neumann ...
This formulation leads to a mapping to statistical mechanics such that the metric learning optimization problem becomes equivalent to free energy minimization.
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What is distance metric learning?
What is the free energy distance?
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Since the distance metric is determined by free energy minimization, we name our method Free Energy Nearest Neighbor (FENN). The algo- rithm for FENN is ...
Missing: Minimization | Show results with:Minimization
One is metric learning (ML), which learns the Mahalanobis distance, parameterized by a semi-definite matrix M, directly from data; the other being LDR methods, ...
Missing: Free | Show results with:Free
Distance metric learning is a category of machine learning algorithms that extracts similarity information from the input features themselves.