Fast decentralized learning of a Gaussian mixture model for large-scale multimedia retrieval

2006 ◽  
Author(s):  
A. Nikseresht ◽  
M. Gelgon
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Large Scale ◽  
Gaussian Mixture ◽  
Multimedia Retrieval ◽  
Decentralized Learning

Reduced Degrees of Freedom Gaussian Mixture Model Fitting for Large Scale History Matching Problems

2019 ◽  
Author(s):  
Guohua Gao ◽  
Hao Jiang ◽  
Chaohui Chen ◽  
Jeroen C. Vink ◽  
Yaakoub El Khamra ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
History Matching ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Model Fitting ◽  
Gaussian Mixture ◽  
Matching Problems

scholarly journals PoSTcode: Probabilistic image-based spatial transcriptomics decoder

2021 ◽  
Author(s):  
Milana Gataric ◽  
Jun Sung Park ◽  
Tong Li ◽  
Vasy Vaskivskyi ◽  
Jessica Svedlund ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Large Scale ◽  
Probabilistic Method ◽  
Gaussian Mixture ◽  
Full Potential ◽  
Correlated Noise ◽  
New Approach ◽  
Tuning Parameters

Realising the full potential of novel image-based spatial transcriptomic (IST) technologies requires robust and accurate algorithms for decoding the hundreds of thousand fluorescent signals each derived from single molecules of mRNA. In this paper, we introduce PoSTcode, a probabilistic method for transcript decoding from cyclic multi-channel images, whose effectiveness is demonstrated on multiple large-scale datasets generated using different versions of the in situ sequencing protocols. PoSTcode is based on a re-parametrised matrix-variate Gaussian mixture model designed to account for correlated noise across fluorescence channels and imaging cycles. PoSTcode is shown to recover up to 50% more confidently decoded molecules while simultaneously decreasing transcript mislabeling when compared to existing decoding techniques. In addition, we demonstrate its increased stability to various types of noise and tuning parameters, which makes this new approach reliable and easy to use in practice. Lastly, we show that PoSTcode produces fewer doublet signals compared to a pixel-based decoding algorithm.

scholarly journals Estimating hotspots using a Gaussian mixture model from large-scale taxi GPS trace data

2019 ◽  
Vol 1 (2) ◽  
pp. 145-153
Author(s):  
Jin-jun Tang ◽  
Jin Hu ◽  
Yi-wei Wang ◽  
He-lai Huang ◽  
Yin-hai Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Large Scale ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Gaussian Mixture ◽  
Traffic Information ◽  
Trace Data ◽  
Real Trace ◽  
Two Parameters

Abstract The data collected from taxi vehicles using the global positioning system (GPS) traces provides abundant temporal-spatial information, as well as information on the activity of drivers. Using taxi vehicles as mobile sensors in road networks to collect traffic information is an important emerging approach in efforts to relieve congestion. In this paper, we present a hybrid model for estimating driving paths using a density-based spatial clustering of applications with noise (DBSCAN) algorithm and a Gaussian mixture model (GMM). The first step in our approach is to extract the locations from pick-up and drop-off records (PDR) in taxi GPS equipment. Second, the locations are classified into different clusters using DBSCAN. Two parameters (density threshold and radius) are optimized using real trace data recorded from 1100 drivers. A GMM is also utilized to estimate a significant number of locations; the parameters of the GMM are optimized using an expectation-maximum (EM) likelihood algorithm. Finally, applications are used to test the effectiveness of the proposed model. In these applications, locations distributed in two regions (a residential district and a railway station) are clustered and estimated automatically.

Reduced-Degrees-of-Freedom Gaussian-Mixture-Model Fitting for Large-Scale History-Matching Problems

SPE Journal ◽  
2019 ◽  
Vol 25 (01) ◽  
pp. 037-055
Author(s):  
Guohua Gao ◽  
Hao Jiang ◽  
Chaohui Chen ◽  
Jeroen C. Vink ◽  
Yaakoub El Khamra ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
History Matching ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
New Method ◽  
Matching Problem ◽  
Matching Problems

Summary It has been demonstrated that the Gaussian-mixture-model (GMM) fitting method can construct a GMM that more accurately approximates the posterior probability density function (PDF) by conditioning reservoir models to production data. However, the number of degrees of freedom (DOFs) for all unknown GMM parameters might become huge for large-scale history-matching problems. A new formulation of GMM fitting with a reduced number of DOFs is proposed in this paper to save memory use and reduce computational cost. The performance of the new method is benchmarked against other methods using test problems with different numbers of uncertain parameters. The new method performs more efficiently than the full-rank GMM fitting formulation, reducing the memory use and computational cost by a factor of 5 to 10. Although it is less efficient than the simple GMM approximation dependent on local linearization (L-GMM), it achieves much higher accuracy, reducing the error by a factor of 20 to 600. Finally, the new method together with the parallelized acceptance/rejection (A/R) algorithm is applied to a synthetic history-matching problem for demonstration.

scholarly journals Outlier Detection in Large-Scale Traffic Data by Naïve Bayes Method and Gaussian Mixture Model Method

2017 ◽  
Vol 2017 (9) ◽  
pp. 73-78 ◽  
Author(s):  
Philip Lam ◽  
Lili Wang ◽  
HenryY.T. Ngan ◽  
NelsonH.C. Yung ◽  
AnthonyG.O. Yeh
Keyword(s):  
Outlier Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Large Scale ◽  
Naive Bayes ◽  
Gaussian Mixture ◽  
Naïve Bayes ◽  
Traffic Data ◽  
Bayes Method ◽  
Model Method
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