SEEK-VEC: Robust Latent Structure Discovery via Ensemble Topic Modeling.

ABSTRACT: Count data are ubiquitous across many applications in which understanding hidden patterns, or latent structure, is of interest. Topic modeling is a powerful tool for detecting latent structure in count data. However, standard topic modeling methods are often constrained by their restrictive assumptions, susceptible to noise, and sensitive to misspecification of the number of topics, which is particularly of concern when analyzing non-text data. Here, we introduce SEEK-VEC (Spectral Ensembling of topic models with Eigenscore for K-agnostic Vocabulary Embedding and Classification), an ensemble framework for count data that integrates insights from multiple candidate topic models through a spectral ensembling procedure. This approach automatically reinforces signal and mitigates noise to gene

SUBMITTER: Danning R 

PROVIDER: S-EPMC12714008 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature