Mixed Membership sub-Gaussian Models
The Gaussian mixture model is widely used in unsupervised learning, owing to its simplicity and interpretability. However, a fundamental limitation of the classical Gaussian mixture model is that it forces each observation to belong to exactly one component. In many practical applications, such as genetics, social network analysis, and text mining, an observation may naturally belong to multiple components or exhibit partial membership in several latent components. To overcome this limitation, we propose the mixed membership sub-Gaussian model, which extends the classical Gaussian mixture framework by allowing each observation to belong to multiple components. This model inherits the inte...
| Authors | Huan Qing |
|---|---|
| Primary category | stat.ML |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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