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. We introduce an efficient likelihood-based framework for AA supporting Bernoulli, Poisson, and multinomial observation models. Our optimization scheme employs local quadratic approximations of the negative log-likelihood, enabling constrained updates through sequential minimal optimization (SMO) and an active-set method. Scalability is improved by bounding the active set w...
| Authors | A. Emilie J. Wedenborg, Jesper Løve Hinrich, Morten Mørup |
|---|---|
| Primary category | stat.ML |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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