Nonlinear Spline Adaptive Filters based on a Low Rank Approximation-Reference-Cited by-同舟云学术

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Nonlinear Spline Adaptive Filters based on a Low Rank Approximation

Published:2022-12 Issue: Volume:201 Page:108726
ISSN:0165-1684
Container-title:Signal Processing
language:en
Short-container-title:Signal Processing

Author:

Bhattacharjee Sankha Subhra^ORCID,Patel Vinal,George Nithin V

Funder

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software,Control and Systems Engineering

Reference19 articles.

1. Kernel adaptive filtering: a comprehensive introduction;Príncipe,2011

2. New iterative kernel algorithms for nonlinear acoustic echo cancellation;Albu,2015

3. Graph kernel recursive least-squares algorithms;Gogineni,2021

4. Graph diffusion kernel LMS using random fourier features;Gogineni,2020

5. Online distributed learning over networks in RKH spaces using random fourier features;Bouboulis;IEEE Transactions on Signal Processing,2017

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1. Decomposition-Based Wiener Filter Using the Kronecker Product and Conjugate Gradient Method;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024

2. Real-Time Spline Adaptive Filter: Design and Efficiency Analysis;Optoelectronics, Instrumentation and Data Processing;2023-12-10

3. Recursive least-squares algorithm based on a third-order tensor decomposition for low-rank system identification;Signal Processing;2023-12

4. Iterative Wiener Filter Based on Third-Order Tensor Decomposition and Coordinate Descent Method;2023 8th International Symposium on Electrical and Electronics Engineering (ISEEE);2023-10-26

5. Study of Sparsity Emanating from NKPD and its Utilization to Enhance NKPD based Adaptive Algorithms;2023 31st European Signal Processing Conference (EUSIPCO);2023-09-04

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