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Huan Qing

5 papers hereh-index 28 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.SI3
  • cs.LG1
  • stat.ML1
same name
  • Huan Qing — 15 papers, h 6
  • Huan Qing — 9 papers, h 3
  • Huan Qing — 6 papers, h 1
  • Huan Qing — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedFinding mixed memberships in categorical data

5 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ML2026

Mixed Membership sub-Gaussian Models

Huan Qing

The Gaussian mixture model is widely used in unsupervised learning, owing to its simplicity and interpretability. However, a fundamental limitation of the classical Gaussian mixtur…

cs.SI2023★ 5 cited

Finding mixed memberships in categorical data

Huan Qing

Latent class analysis, a fundamental problem in categorical data analysis, often encounters overlapping latent classes that introduce further challenges. This paper presents a solu…

cs.LG2023

Latent class analysis by regularized spectral clustering

Huan Qing

The latent class model is a highly effective tool in the analysis of categorical data from social, psychological, and behavioral sciences, where populations often share hidden comm…

cs.SI2023

Latent class analysis with weighted responses

Huan Qing

The latent class model has been proposed as a powerful tool for cluster analysis of categorical data in various fields such as social, psychological, behavioral, and biological sci…

cs.SI2023

Mixed membership estimation for categorical data with weighted responses

Huan Qing

The Grade of Membership (GoM) model, which allows subjects to belong to multiple latent classes, is a powerful tool for inferring latent classes in categorical data. However, its a…

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