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Heat kernel smoothing using Laplace-Beltrami eigenfunctions. [PDF]
Med Image Comput Comput Assist Interv, 2010 We present a novel surface smoothing framework using the Laplace-Beltrami eigenfunctions. The Green's function of an isotropic diffusion equation on a manifold is constructed as a linear combination of the Laplace-Beltraimi operator. The Green's function is then used in constructing heat kernel smoothing.
Seo S, Chung MK, Vorperian HK.
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Fast Kernel Smoothing in R with Applications to Projection Pursuit
Journal of Statistical Software, 2022 This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main kernel computations are implemented in C++, and are wrapped in simple, intuitive and versatile R functions.
David P. Hofmeyr
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Connectome spatial smoothing (CSS): Concepts, methods, and evaluation
NeuroImage, 2022 Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds—if not thousands—of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography ...
Sina Mansour L +3 more
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Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization [PDF]
المجلة العراقية للعلوم الاحصائية, 2020 In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this
Zinah Basheer, Zakariya Algamal
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Distributed Smoothed Tree Kernel [PDF]
Italian Journal of Computational Linguistics, 2014 In this paper we explore the possibility to merge the world of Compositional Distributional Semantic Models (CDSM) with Tree Kernels (TK). In particular, we will introduce a specific tree kernel (smoothed tree kernel, or STK) and then show that is possibile to approximate such kernel with the dot product of two vectors obtained compositionally from the
Ferrone, L, ZANZOTTO, FABIO MASSIMO
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Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models
Revstat Statistical Journal, 2021 This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing
Ersin Yilmaz +2 more
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Equivalent Kernels for Smoothing Splines [PDF]
Journal of Integral Equations and Applications, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Eggermont, P.P.B., LaRiccia, V.N.
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A Fast Two-Stage Bilateral Filter Using Constant Time O(1) Histogram Generation
Sensors, 2022 Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often ...
Sheng-Wei Cheng +2 more
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A new family of kernels from the beta polynomial kernels with applications in density estimation
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko +2 more
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Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI
Sensors, 2023 Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final ...
Cemre Candemir
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