Expectation Maximization method for multivariate change point detection in presence of unknown and changing covariance

Keshavarz, Marziyeh; Huang, Biao

doi:10.1016/j.compchemeng.2014.06.016

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…Until today, there are some particular instances of the EM algorithm for changepoint models available. It has been shown that an EM algorithm can be developed to detect changepoint locations (Keshavarz and Huang, 2014). Bansal et al (2008), in turn, estimate the prior probabilities P (C i = i) in the single changepoint case by means of an EM algorithm.…”

Section: An Em Algorithm For Changepoint Modelsmentioning

confidence: 99%

Bayesian Changepoint Analysis

Siems

2020

Preprint

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Section: An Em Algorithm For Changepoint Modelsmentioning

confidence: 99%

Bayesian Changepoint Analysis

Siems

2020

Preprint

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“…In [43], EM was used to detect possible changes in data as a function of time that has unknown and variable covariances. This study was based on simulated data and conducted in an experimental way and showed that single or multiple changes were satisfactorily detected.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants

Librantz

Santos

2023

Sustainability

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Currently, the use of intelligent models for decision making in the water treatment process is very important, as many plants support their implementation with the aim of obtaining economic, social, and environmental gains. Nevertheless, for these systems to be properly modeled, the data should be carefully selected so that only those that represent good operating practices are used. Thus, this study proposes an approach for identifying water quality and operational scenarios using the expectation maximisation (EM) and self-organising maps (SOMs) techniques when using data from a water treatment plant. The results showed that both techniques were able to identify quantities of different scenarios, some similar and others different, allowing for the evaluation of differences in a robust way. The EM technique resulted in fewer scenarios when compared with the SOMs technique, including in the cluster selection process. The results also indicated that an intelligent model can be trained with data from the proposed clustering, which improves its prediction capacity under different operating conditions; this can lead to savings in chemical product usage and less waste generation throughout the water treatment process, which is in good agreement with cleaner production practices.

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Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling

et al. 2015

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Latent variable (LV) models provide explicit representations of underlying driving forces of process variations and retain the dominant information of process data. In this study, slow features (SFs) as temporally correlated LVs are derived using probabilistic SF analysis. SFs evolving in a state‐space form effectively represent nominal variations of processes, some of which are potentially correlated to quality variables and hence help improving the prediction performance of soft sensors. An efficient expectation maximum algorithm is proposed to estimate parameters of the probabilistic model, which turns out to be suitable for analyzing massive process data. Two criteria are also proposed to select quality‐relevant SFs. The validity and advantages of the proposed method are demonstrated via two case studies. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4126–4139, 2015

show abstract

Expectation Maximization method for multivariate change point detection in presence of unknown and changing covariance

Cited by 8 publications

References 31 publications

Bayesian Changepoint Analysis

Bayesian Changepoint Analysis

Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants

Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling

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