Suchergebnisse - Digital Signal Processing with Kernel Methods

Digital Signal Processing with Kernel Methods von Rojo-Álvarez, José Luis, Martínez-Ramón, Manel, Muñoz-Mar&iacute, Jordi, Camps-Valls, Gustau

“… to communications, multimedia, and biomedical engineering systems </b> <p><i> Digital Signal Processing with Kernel Methods</i&gt …”
Volltext

Built-in self-scaling method for kernel-based estimation in the presence of nonlinear distortion von Tan, Ai Hui

“… extension of the standard kernel-based (KB) formulation into the built-in self-scaling (BS) method. Theoretical derivation proves that the BS method can preserve the property of harmonic suppression in perturbation signals …”
Volltext

ECG biometric authentication based on non-fiducial approach using kernel methods von Hejazi, Maryamsadat, Al-Haddad, S.A.R., Singh, Yashwant Prasad, Hashim, Shaiful Jahari, Abdul Aziz, Ahmad Fazli

“… ) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods …”
Volltext

Half-quadratic Student's t-based kernel adaptive filter based on multikernel Nyström method von Cui, Mingjing, Lin, Dongyuan, Zheng, Yunfei, Wang, Shiyuan

“… Kernel adaptive filters (KAFs) are effective for nonlinear signal processing. However, the performance of KAFs based on the minimum mean square error (MMSE …”
Volltext

A low-rate identification method for digital predistorters based on Volterra kernel interpolation von Singerl, Peter, Koeppl, Heinz

“… (digital predistortion) and mixed signal simulations require system models at a higher sampling rate than Nyquist …”
Volltext

Kernel Regression for Image Processing and Reconstruction von Takeda, H., Farsiu, S., Milanfar, P.

“… In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction …”
Volltext

Sparse Representation With Kernels von Shenghua Gao, Tsang, I. W., Liang-Tien Chia

“… Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR …”
Volltext

Processing Near Sensor Architecture in Mixed-Signal Domain With CMOS Image Sensor of Convolutional-Kernel-Readout Method von Chen, Zhe, Zhu, Huifeng, Ren, Erxiang, Liu, Zheyu, Jia, Kaige, Luo, Li, Zhang, Xuan, Wei, Qi, Qiao, Fei, Liu, Xinjun, Yang, Huazhong

“… In this paper, a processing near sensor architecture in mixed-signal domain with CMOS Image Sensor (CIS …”
Volltext

Kernel adaptive filtering: a comprehensive introduction von Príncipe, J. C, Liu, Weifeng, Haykin, Simon S

“… Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced …”
Volltext

Robust Kernel Representation With Statistical Local Features for Face Recognition von Meng Yang, Lei Zhang, Shiu, Simon Chi-Keung, Zhang, D.

“… classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF …”
Volltext

Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning von Gao, Xinbo, Gao, Fei, Tao, Dacheng, Li, Xuelong

“… Universal blind image quality assessment (IQA) metrics that can work for various distortions are of great importance for image processing systems …”
Volltext

ViBe: A Universal Background Subtraction Algorithm for Video Sequences von Barnich, O, Van Droogenbroeck, M

“… This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each …”
Volltext

KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition von Jian Yang, Frangi, A.F., Jing-Yu Yang, David Zhang, Zhong Jin

“… This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e …”
Volltext

Multivariate online kernel density estimation with Gaussian kernels von Kristan, Matej, Leonardis, Aleš, Skočaj, Danijel

“… We propose a novel approach to online estimation of probability density functions, which is based on kernel density estimation (KDE …”
Volltext

Design of Mittag–Leffler Kernel-Based Fractional-Order Digital Filter Using Fractional Delay Interpolation von Gupta, Anmol, Kumar, Sanjay

“… –Leffler kernel-based Atangana–Baleanu–Caputo (ABC) fractional-order digital filter (FODF) has been proposed. The design has been obtained by first numerically …”
Volltext

Soft Margin Multiple Kernel Learning von Xinxing Xu, Tsang, I. W., Dong Xu

“… Multiple kernel learning (MKL) has been proposed for kernel methods by learning the optimal kernel from a set of predefined base kernels …”
Volltext

A quantum Jensen–Shannon graph kernel for unattributed graphs von Bai, Lu, Rossi, Luca, Torsello, Andrea, Hancock, Edwin R.

“… In this paper, we use the quantum Jensen–Shannon divergence as a means of measuring the information theoretic dissimilarity of graphs and thus develop a novel graph kernel …”
Volltext

Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis von Zhu, Xiaofeng, Huang, Zi, Tao Shen, Heng, Cheng, Jian, Xu, Changsheng

“… In this paper, we propose a novel method named Mixed Kernel CCA (MKCCA) to achieve easy yet accurate implementation of dimensionality reduction …”
Volltext

Online Kernel Principal Component Analysis: A Reduced-Order Model von Honeine, P.

“… Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one of the most used data analysis and dimensionality reduction techniques, the principal component analysis …”
Volltext

Anisotropic Gaussian kernel adaptive filtering by Lie-group dictionary learning von Wada, Tomoya, Fukumori, Kosuke, Tanaka, Toshihisa, Fiori, Simone

“… The present paper proposes a novel kernel adaptive filtering algorithm, where each Gaussian kernel is parameterized by a center vector and a symmetric positive definite (SPD …”
Volltext