Probabilistic Latent Semantic Indexing (pLSI) – Lecture Notes

DeLaRu77 ] Choose \(k\) centers randomly, unit covariance, and uniform weight: \[\theta = (\mu _1, \Sigma _1, w_1, \mu _2, \Sigma _2, w_2, \ldots \mu _k, \Sigma _k, w_k)\] Expect cluster labels based on Gaussian [...] \sum\nolimits_t \theta _{d,t} = 1 \\ \beta _{t,w} \propto \, \, & \sum\nolimits_d\, \operatorname {tf}_{w,d} P(z_{d,w}=t) & \text{s.t. }\forall_t:& \sum\nolimits_w \beta _{t,w} = 1 \end{align*}\] Note: because [...] algorithm. Journal of the Royal Statistical Society: Series B (Statistical Methodology) . 39, 1 (1977), 1–31. [Hofm99a] Hofmann, T. 1999. Learning the similarity of documents: An information-geometric …