K-means feature extractor as a minimal mixture of factor analyzers, explained
Fowe · x · 2026-11-12
In a technical exchange, caglaree explains that the feature-extractor extension of K-means is essentially unsupervised learning, viewable as a minimal mixture of factor analyzers that only makes sense when variational posteriors are coupled with extra constraints. Since self-supervised learning can be seen as a form of supervised learning mathematically, the approach also works for self-supervised setups, including generating random inputs by sampling latents from the output GMM.
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