Introduces a topological regularization framework for NMF that uses persistent homology to enforce desired structures in basis functions within a unified optimization objective.
Perea and John Harer
3 Pith papers cite this work. Polarity classification is still indexing.
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A standardized pipeline converts time series to graphs, computes persistence diagrams, and extracts features that classify UCR benchmarks, with diffusion distance outperforming shortest-path metrics and performance varying by graph type.
Hybrid models that add persistent-homology features from fixation time series to traditional statistical features outperform purely statistical baselines for dyslexia detection on the Copenhagen Corpus.
citing papers explorer
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Non-negative Matrix Factorisation with Topological Regularisation
Introduces a topological regularization framework for NMF that uses persistent homology to enforce desired structures in basis functions within a unified optimization objective.
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Persistent Homology of Time Series through Complex Networks
A standardized pipeline converts time series to graphs, computes persistence diagrams, and extracts features that classify UCR benchmarks, with diffusion distance outperforming shortest-path metrics and performance varying by graph type.
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Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection
Hybrid models that add persistent-homology features from fixation time series to traditional statistical features outperform purely statistical baselines for dyslexia detection on the Copenhagen Corpus.