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papersSEP 10 04:00 UTC

Deep Fréchet Neural Network Framework Proposed for Metric-Space-Valued Regression

Researchers introduce DFNN, a deep neural network framework designed for regression tasks where the response variable lies in a general metric space rather than Euclidean coordinates. The approach targets non-Euclidean outputs such as probability distributions, networks, symmetric positive-definite matrices, and compositional data. It extends Fréchet regression concepts into deep learning architectures for these increasingly common data types.

DFNN Fréchet regression compositional data metric-space-valued regression non-Euclidean data symmetric positive-definite matrices

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arXiv cs.LGDFNN: A Deep Fr\'echet Neural Network Framework for Learning Metric-Space-Valued Responses ↗SEP 10 04:00 UTC