Density-Preserving Hierarchical EM Algorithm : Simplifying Gaussian Mixture Models for Approximate Inference
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Pages (from-to) | 1323-1337 |
Journal / Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 41 |
Issue number | 6 |
Online published | 7 Jun 2018 |
Publication status | Published - Jun 2019 |
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DOI | DOI |
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Attachment(s) | Documents
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85048462417&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(954e1af0-8316-4500-891d-a1bd3a19b80a).html |
Abstract
We propose an algorithm for simplifying a finite mixture model into a reduced mixture model with fewer mixture components. The reduced model is obtained by maximizing a variational lower bound of the expected log-likelihood of a set of virtual samples. We develop three applications for our mixture simplification algorithm: recursive Bayesian filtering using Gaussian mixture model posteriors, KDE mixture reduction, and belief propagation without sampling. For recursive Bayesian filtering, we propose an efficient algorithm for approximating an arbitrary likelihood function as a sum of scaled Gaussian. Experiments on synthetic data, human location modeling, visual tracking, and vehicle self-localization show that our algorithm can be widely used for probabilistic data analysis, and is more accurate than other mixture simplification methods.
Research Area(s)
- Approximation algorithms, Bayes methods, Clustering algorithms, density simplification, Gaussian mixture model, Inference algorithms, likelihood approximation, Mixture models, recursive Bayesian filtering
Citation Format(s)
Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference. / Yu, Lei; Yang, Tianyu; Chan, Antoni B.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, No. 6, 06.2019, p. 1323-1337.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, No. 6, 06.2019, p. 1323-1337.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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