Reducing the cost of fitting mixture models via stochastic sampling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00364108" target="_blank" >RIV/68407700:21230/22:00364108 - isvavai.cz</a>
Result on the web
<a href="https://openreview.net/forum?id=WPWcn80R36y" target="_blank" >https://openreview.net/forum?id=WPWcn80R36y</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Reducing the cost of fitting mixture models via stochastic sampling
Original language description
Traditional methods for unsupervised learning of finite mixture models require to evaluate the likelihood of all components of the mixture. This quickly becomes prohibitive when the components are abundant or expensive to compute. Therefore, we propose to apply a combination of the expectation maximization and the Metropolis-Hastings algorithm to evaluate only a small number of, stochastically sampled, components, thus substantially reducing the computational cost. The Markov chain of component assignments is sequentially generated across the algorithm's iterations, having a non-stationary target distribution whose parameters vary via a gradient-descent scheme. We put emphasis on generality of our method, equipping it with the ability to train mixture models which involve complex, and possibly nonlinear, transformations. The performance of our method is illustrated on mixtures of normalizing flows.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-32620S" target="_blank" >GA22-32620S: Unsupervised learning from heterogenous structured data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů