arXiv Machine Learning June 16, 2026 By Jernej Grlj, Aaron D. Lauda

Analytic Torsion and Spectral Gap Capture Persistent-Laplacian Performance

Read the original on arXiv Machine Learning →

arXiv:2606. 16990v1 Announce Type: new Abstract: While persistent Laplacians (PL) offer a richer geometric representation of data than persistent homology, utilizing their full eigenspectrum for learning tasks is often hampered by high dimensionality and the ``varying length'' problem across different filtration scales.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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