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Jesper L. Hinrich

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#machine learning Preprint Oct 2026

Efficient and Generalizable Archetypal Analysis for Discrete Data

Archetypal Analysis (AA) represents observations as convex combinations of extremal data-driven profiles, yielding interpretable low-dimensional descriptions of complex datasets. Classical AA relies on a least-squares objective, which is poorly suited to discrete observations such as binary, count, and categorical data...

A. E. J. Wedenborg, Jesper L. Hinrich, Morten Mørup · 0 citations
Oct 2026

Multiway Modeling of 2D NMR Spectra for Enhanced Chemical Characterization of Complex Mixtures

One-dimensional (1D) proton nuclear magnetic resonance (NMR) spectroscopy is widely used for complex mixture analysis because of its high reproducibility, minimal sample preparation, and inherent quantitative capabilities. However, 1D NMR remains constrained by three interrelated limitations: signal overlap among com...

Pia S. Mayer, S. B. Engelsen, Jesper L. Hinrich et al. · 0 citations
Preprint Aug 2026

Exploiting chemical shift variability enables recovery of overlapping metabolites from 1H nuclear magnetic resonance spectra

Overlapping peaks and sample-dependent chemical shift variability prevent reliable metabolite recovery from complex biological spectra. This problem is critical in one-dimensional proton (1D 1H) NMR which has become the standard method providing fast acquisition and information-rich spectra in metabolomics and foodomic...

Jesper L. Hinrich, Pia S. Mayer, Bekzod Khakimov et al. · 0 citations

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