D. Forster

activity

20152025

most cited

k

-means as a variational EM approximation of Gaussian mixture models

53 citations · 89 across the 7 of their papers we have counts for

collaborators

Showing 2017 · stat.MLShow all

3 papers · 2 filters

stat.ML2017★ 8 cited

Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and $k$ -means

Dennis Forster, Jörg Lücke

One iteration of standard $k$ -means (i.e., Lloyd's algorithm) or standard EM for Gaussian mixture models (GMMs) scales linearly with the number of clusters $C$ , data points $N$ , an…

stat.ML2017★ 53 cited

$k$ -means as a variational EM approximation of Gaussian mixture models

Jörg Lücke, Dennis Forster

We show that $k$ -means (Lloyd's algorithm) is obtained as a special case when truncated variational EM approximations are applied to Gaussian Mixture Models (GMM) with isotropic Ga…

stat.ML2017★ 2 cited

Truncated Variational EM for Semi-Supervised Neural Simpletrons

Dennis Forster, Jörg Lücke

Inference and learning for probabilistic generative networks is often very challenging and typically prevents scalability to as large networks as used for deep discriminative appro…

Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means

k-means as a variational EM approximation of Gaussian mixture models

Truncated Variational EM for Semi-Supervised Neural Simpletrons

Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and $k$ -means

$k$ -means as a variational EM approximation of Gaussian mixture models