Dimensionality reduction using a Gaussian Process Annealed Particle Filter for tracking and classification of articulated body motions

2011 ◽  
Vol 115 (4) ◽  
pp. 503-519 ◽  
Author(s):  
Leonid Raskin ◽  
Michael Rudzsky ◽  
Ehud Rivlin
Keyword(s):  
Dimensionality Reduction ◽  
Gaussian Process ◽  
Particle Filter ◽  
Annealed Particle Filter

scholarly journals Efficient Dimensionality Reduction Methods in Reservoir History Matching

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3137
Author(s):  
Amine Tadjer ◽  
Reider B. Bratvold ◽  
Remus G. Hanea
Keyword(s):  
Data Assimilation ◽  
Dimensionality Reduction ◽  
Gaussian Process ◽  
Latent Variable ◽  
History Matching ◽  
Production Performance ◽  
Latent Variable Model ◽  
Variable Model ◽  
Multiple Data ◽  
Ensemble Smoother

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.

scholarly journals Nonlinear Classification of EEG recordings from patients with Alzheimer’s Disease using Gaussian Process Latent Variable Model

2020 ◽  
Author(s):  
S. Rajintha. A. S. Gunawardena ◽  
Fei He ◽  
Ptolemaios Sarrigiannis ◽  
Daniel J. Blackburn
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gaussian Process ◽  
Manifold Learning ◽  
Latent Variable ◽  
Time Window ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Variable Model

AbstractIn this work, nonlinear temporal features from multi-channel EEGs are used for the classification of Alzheimer’s disease patients from healthy individuals. This was achieved by temporal manifold learning using Gaussian Process Latent Variable Models (GPLVM) as a nonlinear dimensionality reduction technique. Classification of the extracted features was undertaken using a nonlinear Support Vector Machine. Comparisons were made against the linear counterpart, Principle Component Analysis while exploring the effect of the time window or EEG epoch length used. It was demonstrated that temporal manifold learning using GPLVM is better in extracting features that attain high separability and prediction accuracy. This work aims to set the significance of using GPLVM temporal manifold learning for EEG feature extraction in the classification of Alzheimer’s disease.

scholarly journals Benchmarking joint multi-omics dimensionality reduction approaches for cancer study

2020 ◽  
Author(s):  
Laura Cantini ◽  
Pooya Zakeri ◽  
Celine Hernandez ◽  
Aurelien Naldi ◽  
Denis Thieffry ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Ground Truth ◽  
Systematic Evaluation ◽  
Omics Data ◽  
Biological Processes ◽  
Cancer Data ◽  
Practical Guidelines ◽  
Cell Data ◽  
Omics Data Integration

AbstractHigh-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve this multi-omics data integration, Joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines.We performed a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluated their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we used TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assessed their classification of multi-omics single-cell data.From these in-depth comparisons, we observed that intNMF performs best in clustering, while MCIA offers a consistent and effective behavior across many contexts. The full code of this benchmark is implemented in a Jupyter notebook - multi-omics mix (momix) - to foster reproducibility, and support data producers, users and future developers.

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