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Central node identification via weighted kernel density estimation
The detection of central nodes in a network is a fundamental task in network science and graph data analysis. During the past decades, numerous...
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A reliable data-based smoothing parameter selection method for circular kernel estimation
A new data-based smoothing parameter for circular kernel density (and its derivatives) estimation is proposed. Following the plug-in ideas, unknown...
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Class specific nullspace marginal discriminant analysis with overfitting-prevention kernel estimation for hand trajectory recognitions
Hand trajectories are widely used for gesture recognition, action analysis, and sign language translation. Effective hand trajectory feature...
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Nonparametric Bayesian online change point detection using kernel density estimation with nonparametric hazard function
This paper aims to develop Bayesian online change point detection (BOCD), a parametric change point detection method, into a nonparametric method to...
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Two-stage single image Deblurring network based on deblur kernel estimation
Image deblurring for dynamic scenes is a serious challenge in computer vision. Motion blur is caused by camera shaking or object movement during the...
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Sparse Adaptive Channel Estimation Based on Multi-kernel Correntropy
The communication channel estimation between unmanned systems has always been a concern of researchers, especially the channel estimation of...
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Optical flow estimation via weighted guided filtering with non-local steering kernel
The weighted median filter and the guided image filter are considered important methods for the recently popular variational and non-local total...
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Kernel Learning Estimation: A Model-Free Approach to Tracking Randomly Moving Object
Kernel learning estimation (KLE) is a kernel-based method, where the original spatial data is mapped into a high-dimensional Hilbert space by a...
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Fast Estimation of Multidimensional Regression Functions by the Parzen Kernel-Based Method
Various methods for estimation of unknown functions from the set of noisy measurements are applicable to a wide variety of problems. Among them the...
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GAN for Blind Image Deblurring Based on Latent Image Extraction and Blur Kernel Estimation
We propose a GAN for image deblurring based on latent image extraction and blur kernel estimation, with which the single image deblurring assignment...
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LBKENet:Lightweight Blur Kernel Estimation Network for Blind Image Super-Resolution
Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its...
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Isolation Kernel Estimators
Existing adaptive kernel density estimators (KDEs) and kernel regressions (KRs) often employ a data-independent kernel, such as Gaussian kernel. They...
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Contrastive Kernel Subspace Clustering
As a class of nonlinear subspace clustering methods, kernel subspace clustering has shown promising performance in many applications. This paper...
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Generalized complex kernel least-mean-square algorithm with adaptive kernel widths
A novel variable kernel width generalized complex-valued least mean-square (VKW-GCKLMS) algorithm aims to optimize kernel width in online way to...
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Unfolded Deep Kernel Estimation for Blind Image Super-Resolution
Blind image super-resolution (BISR) aims to reconstruct a high-resolution image from its low-resolution counterpart degraded by unknown blur kernel...
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Fast Kernel Density Estimation with Density Matrices and Random Fourier Features
Kernel density estimation (KDE) is one of the most widely used nonparametric density estimation methods. The fact that it is a memory-based method,...
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Frequency component Kernel for SVM
Finding a proper kernel for Support vector machine and adjusting the involved parameters for a better classification remain immense challenges. This...
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Protecting Kernel Code Integrity with PMP on RISC-V
Kernel code integrity is the foundation of the security of the entire system. Attackers are motivated to compromise the kernel code integrity because...
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Lane line detection and departure estimation in a complex environment by using an asymmetric kernel convolution algorithm
Deep learning has made tremendous advances in the domains of image segmentation and object classification. However, real-time lane line detection and...
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