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COMPLEX: A Closed-Form Certified Embedding of Multiparameter Persistence Modules

Majhi, Sushovan et al.
arXiv (All) · Papers
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62h30algebraic-topologyg-2-1i-5-1machine-learning
Every multiparameter persistence vectorization we know of carries a one-sided Lipschitz upper bound and nothing below it: without a lower gauge there is no sense in which the features are faithful, and no per-prediction guarantee can be built on them. This paper supplies the missing side. COMPLEX is
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