Gaussian Process Regression Method for Classification for High-Dimensional Data with Limited Samples

Zhang, Nian; Xiong, Jiang; Zhong, Jue; Leatham, Keenan

doi:10.1109/icist.2018.8426077

Cited by 59 publications

(32 citation statements)

References 6 publications

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“…The variation obtained from TOPSIS using the data forecasted using DT, SVM, and GPR are shown in Figure 2, Figure 3, and Figure 4, respectively. For detailed understanding of DT, SVM and GPR refer the study of Zhang et. al., (2018), Pelckmans et al, (2002), and Safavian and Landgrebe, D. (1991) respectilvey.…”

Section: Analysis Results and Discussionmentioning

confidence: 99%

Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

Kumar

Khan

Gupta

et al. 2020

Preprint

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Section: Analysis Results and Discussionmentioning

confidence: 99%

Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

Kumar

Khan

Gupta

et al. 2020

Preprint

View full text Add to dashboard Cite

“…In addition, we have evaluated three non-linear regression models. The Gaussian Process Regression (GPR) models are non-parametric, Bayesian approaches for a supervised learning problem with a set of random variables where any finite number of them have a joint Gaussian distribution [ 52 , 53 ]. One major limitation of GPR is the computational cost for large datasets [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment

Cohen

Fernie

Fekr

2020

IJERPH

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Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device’s error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method.

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“…We compared common machine learning algorithms and used different kernel functions. The performance gaussian process regression [8][9][10][11] with kernels [12] of rational quadratic (Quadratic), squared exponential (Squared Exp), matern 5/2 and exponential is shown in Fig.6 (c). The error and accuracy of support vector machine [13,14] with different kernels [15] of linear, quadratic, cubic, fine gaussian (Fine), medium gaussian (Medium) and coarse gaussian (Coarse) is given in Fig.6 (d).…”

Section: Force Measurement Testbed and Experiments Setupmentioning

confidence: 99%

“…The performance gaussian process regression [8][9][10][11] with kernels [12] of rational quadratic (Quadratic), squared exponential (Squared Exp), matern 5/2 and exponential is shown in Fig.6 (c). The error and accuracy of support vector machine [13,14] with different kernels [15] of linear, quadratic, cubic, fine gaussian (Fine), medium gaussian (Medium) and coarse gaussian (Coarse) is given in Fig.6 (d). And Fig.6 (e) shows the performance of different algorithms, including exponential gaussian process regression (GPR), medium gaussian support vector machine (SVM), fine tree (FT) [16], boosted trees (BOT) [17], bagged trees (BAT) [18] and random forest (RF) [19,20].…”

Section: Force Measurement Testbed and Experiments Setupmentioning

confidence: 99%

A Force–Voltage Responsivity Stabilization Method for Piezoelectric Touch Panels in the Internet of Things

Gao¹,

Shao²,

Guo³

et al. 2020

Preprint

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Piezoelectric force touch panels are extensively utilized as human-machine interfaces for 3-dimensional touch sensing in internet of things (IoT) applications. However, the unstable force voltage responsivity issue induced by different touch orientations limits the successful use of piezoelectric touch panels. In this article, a piezoelectric touch panel, which is sensitive to both capacitive and force stimulation, is assembled; and a touch orientation classification technique is developed to calibrate the detected force amplitude by training a machine learning model with finger induced capacitive information. Finally, a high stable force voltage responsivity of 87.5% is achieved experimentally.

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Gaussian Process Regression Method for Classification for High-Dimensional Data with Limited Samples

Cited by 59 publications

References 6 publications

Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment

A Force–Voltage Responsivity Stabilization Method for Piezoelectric Touch Panels in the Internet of Things

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