A Variational EM Acceleration for Efficient Clustering at Very Large Scales

Hirschberger, Florian; Forster, Dennis; Lücke, Jörg

doi:10.1109/tpami.2021.3133763

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…This paper introduced a novel clustering algorithm based on GMMs and tailored for large streams of events. The speed and computational efficiency of the proposed algorithm are significantly increased without adding complex extraneous computation unlike other efficient approximations found in the literature [41]. The algorithm is also more stable than k-means in recovering cluster centers which is important for reproducibility.…”

Section: Discussionmentioning

confidence: 93%

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Efficient spatio-temporal feature clustering for large event-based datasets

Oubari

Exarchakis²,

Lenz³

et al. 2022

Neuromorph. Comput. Eng.

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Event-based cameras encode changes in a visual scene with high temporal precision and low power consumption, generating millions of events per second in the process. Current event-based processing algorithms do not scale well in terms of runtime and computational resources when applied to a large amount of data. This problem is further exacerbated by the development of high spatial resolution vision sensors. We introduce a fast and computationally efficient clustering algorithm that is particularly designed for dealing with large event-based datasets. The approach is based on the Expectation-Maximization (EM) algorithm and relies on a stochastic approximation of the E-step over a truncated space to reduce the computational burden and speed up the learning process. We evaluate the quality, complexity, and stability of the clustering algorithm on a variety of large event-based datasets, and then validate our approach with a classification task. The proposed algorithm is significantly faster than standard k-means and reduces computational demands by two to three orders of magnitude while being more stable, interpretable, and close to the state of the art in terms of classification accuracy.

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Section: Discussionmentioning

confidence: 93%

“…As datasets grow larger, it is increasingly difficult to apply traditional clustering methods and improvements in computational complexity are all the more necessary. Novel algorithms for training mixture models with increased efficiency [40,41] are particularly promising for learning from large datasets.…”

Section: Gmms and Eventsmentioning

confidence: 99%

Efficient spatio-temporal feature clustering for large event-based datasets

Oubari

Exarchakis²,

Lenz³

et al. 2022

Neuromorph. Comput. Eng.

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“…The EM algorithm has an O(nkd) complexity per iteration [ 9 ]. It can be updated on a regular basis, but this does not lead to an explosion of the computation time considering the number of patients entering the intensive care unit for COVID-19.…”

Section: Resultsmentioning

confidence: 99%

Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning

Boussen¹,

Cordier²,

Malet³

et al. 2022

Computers in Biology and Medicine

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Background We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO 2 ) signals. Methods We recorded the BF and SpO 2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S 24 severity score that represented the patient's severity over the previous 24 h; the validity of MS 24 , the maximum S 24 score, was checked against rates of intubation risk and prolonged ICU stay. Results Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S 24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS 24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS 24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87). Conclusions Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.

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“…Several papers propose so-called generalized EM algorithms [34,42,46,52]. The key idea of these generalizations is to replace the maximization steps ( 3) and ( 4) by increase steps.…”

Section: Remark 3 (Generalized Em Algorithms)mentioning

confidence: 99%

Mixed noise and posterior estimation with conditional deepGEM

Hagemann,

Hertrich,

Casfor

et al. 2024

Mach. Learn.: Sci. Technol.

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We develop an algorithm for jointly estimating the posterior and the noise parameters in Bayesian inverse problems, which is motivated by indirect measurements and applications from nanometrology with a mixed noise model. We propose to solve the problem by an expectation maximization (EM) algorithm. Based on the current noise parameters, we learn in the E-step a conditional normalizing flow that approximates the posterior. In the M-step, we propose to find the noise parameter updates again by an EM algorithm, which has analytical formulas. We compare the training of the conditional normalizing flow with the forward and reverse Kullback–Leibler divergence, and show that our model is able to incorporate information from many measurements, unlike previous approaches.

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A Variational EM Acceleration for Efficient Clustering at Very Large Scales

Cited by 7 publications

References 49 publications

Efficient spatio-temporal feature clustering for large event-based datasets

Efficient spatio-temporal feature clustering for large event-based datasets

Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning

Mixed noise and posterior estimation with conditional deepGEM

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