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Symmetry
Special Issues
Adaptive Filtering and Machine Learning
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Adaptive Filtering and Machine Learning

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 7228

Special Issue Editors

Prof. Dr. Shiyuan Wang

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Interests: statistical signal processing; adaptive filtering; simultaneous localization and mapping (SLAM)
Special Issues, Collections and Topics in MDPI journals
Dr. Guobing Qian

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Interests: adaptive filtering; complex signal processing; blind signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, adaptive filtering and machining learning are related, and have been widely used in practice. Adaptive filtering is mainly used for online applications, and machine learning is used for offline applications. The Journal Symmetry dominantly focuses on natural sciences, and its subject named Computer and Engineering Science is the area which also exploits statistical signal processing similar as adaptive filtering and machine learning. The relation between adaptive filtering and machine learning is an interesting topic that is worth to exploit the advanced signal processing methods, efficiently. The methods developed in adaptive filtering and machine learning can be used to effectively address the problems of denoising for audio signal and image, system identification, channel equalization, navigation, recognition and classification of signals including speech, audio, image, and video, and distributed estimation, and so on. The symmetry and asymmetry regarding the loss function and network structure can also be future directions for combination of adaptive filtering and machine learning in practical applications. The obtained methods will promote the mutual development of adaptive filtering and machine learning.

This special issue not only welcomes original research and review articles regarding recent developments of adaptive filtering and machine learning, but also an overview on the applications of these two topics. Potential topics include but are not limited to the following:

Keywords: Adaptive filter, Kalman filter, Robustness, Noise, Supervised learning, Graphical signal processing, Complex signal, Quaternion adaptive filter, Echo cancellation, Speech recognition, Quantization.

Prof. Dr. Shiyuan Wang
Prof. Dr. Guobing Qian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • adaptive filter
  • kalman filter
  • robustness
  • noise
  • supervised learning
  • graphical signal processing
  • complex signal
  • quaternion adaptive filter
  • echo cancellation
  • speech recognition
  • quantization

Published Papers (5 papers)

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Research

20 pages, 2909 KiB  
Article
Matrix Factorization Recommendation Algorithm Based on Attention Interaction
by Chengzhi Mao, Zhifeng Wu, Yingjie Liu and Zhiwei Shi
Symmetry 2024, 16(3), 267; https://doi.org/10.3390/sym16030267 - 22 Feb 2024
Viewed by 802
Abstract
Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. [...] Read more.
Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item. Full article
(This article belongs to the Special Issue Adaptive Filtering and Machine Learning)
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13 pages, 1007 KiB  
Article
Variable Matrix-Type Step-Size Affine Projection Sign Algorithm for System Identification in the Presence of Impulsive Noise
by Jaewook Shin, Bum Yong Park, Won Il Lee and Jinwoo Yoo
Symmetry 2022, 14(10), 1985; https://doi.org/10.3390/sym14101985 - 22 Sep 2022
Cited by 3 | Viewed by 1079
Abstract
This paper presents a novel variable matrix-type step-size affine projection sign algorithm (VMSS-APSA) characterized by robustness against impulsive noise. To mathematically derive a matrix-type step size, VMSS-APSA utilizes mean-square deviation (MSD) for the modified version of the original APSA. Accurately establishing the MSD [...] Read more.
This paper presents a novel variable matrix-type step-size affine projection sign algorithm (VMSS-APSA) characterized by robustness against impulsive noise. To mathematically derive a matrix-type step size, VMSS-APSA utilizes mean-square deviation (MSD) for the modified version of the original APSA. Accurately establishing the MSD of APSA is impossible. Therefore, the proposed VMSS-APSA derives the upper bound of the MSD using the upper bound of the L1-norm of the measurement noise. The optimal matrix-type step size is calculated at each iteration by minimizing the upper bound of the MSD, thereby improving the filter performance in terms of convergence rate and steady-state estimation error. Because a novel cost function of the proposed VMSS-APSA was designed to maintain a form similar to the original APSA, they have symmetric characteristics. Simulation results demonstrate that the proposed VMSS-APSA improves filter performance in a system-identification scenario in the presence of impulsive noise. Full article
(This article belongs to the Special Issue Adaptive Filtering and Machine Learning)
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21 pages, 23878 KiB  
Article
Distributed Integrated Synthetic Adaptive Multi-Objective Reactive Power Optimization
by Jiayin Song, Chao Lu, Qiang Ma, Hongwei Zhou, Qi Yue, Qinglin Zhu, Yue Zhao, Yiming Fan and Qiqi Huang
Symmetry 2022, 14(6), 1275; https://doi.org/10.3390/sym14061275 - 20 Jun 2022
Viewed by 1233
Abstract
Reactive power is the core problem of voltage stability and economical operation in power systems. Aiming at the problem that multi-objective normalization reactive power optimization function is dependent on weight, an integrated synthesis of adaptive multi-objective particle swarm optimization (ISAMOPSO) is proposed to [...] Read more.
Reactive power is the core problem of voltage stability and economical operation in power systems. Aiming at the problem that multi-objective normalization reactive power optimization function is dependent on weight, an integrated synthesis of adaptive multi-objective particle swarm optimization (ISAMOPSO) is proposed to achieve weight adaptive. Through seven test functions and three algorithm comparison experiments, it is proved that the ISAMOPSO algorithm has stronger global search capability and better convergence. Considering the optimal access position and capacity of distributed generation (DG), the ISAMOPSO algorithm is used for three-objective reactive power optimization. Finally, the results indicate that the ISAMOPSO algorithm can not only provide a variety of optimization schemes to meet different needs, but also realize dynamic reactive power optimization, which further proves that the algorithm can provide effective technical support for solving reactive power optimization problems in practical engineering. Full article
(This article belongs to the Special Issue Adaptive Filtering and Machine Learning)
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24 pages, 435 KiB  
Article
Lagrangian Regularized Twin Extreme Learning Machine for Supervised and Semi-Supervised Classification
by Jun Ma and Guolin Yu
Symmetry 2022, 14(6), 1186; https://doi.org/10.3390/sym14061186 - 09 Jun 2022
Cited by 2 | Viewed by 1487
Abstract
Twin extreme learning machine (TELM) is a phenomenon of symmetry that improves the performance of the traditional extreme learning machine classification algorithm (ELM). Although TELM has been widely researched and applied in the field of machine learning, the need to solve two quadratic [...] Read more.
Twin extreme learning machine (TELM) is a phenomenon of symmetry that improves the performance of the traditional extreme learning machine classification algorithm (ELM). Although TELM has been widely researched and applied in the field of machine learning, the need to solve two quadratic programming problems (QPPs) for TELM has greatly limited its development. In this paper, we propose a novel TELM framework called Lagrangian regularized twin extreme learning machine (LRTELM). One significant advantage of our LRTELM over TELM is that the structural risk minimization principle is implemented by introducing the regularization term. Meanwhile, we consider the square of the l2-norm of the vector of slack variables instead of the usual l1-norm in order to make the objective functions strongly convex. Furthermore, a simple and fast iterative algorithm is designed for solving LRTELM, which only needs to iteratively solve a pair of linear equations in order to avoid solving two QPPs. Last, we extend LRTELM to semi-supervised learning by introducing manifold regularization to improve the performance of LRTELM when insufficient labeled samples are available, as well as to obtain a Lagrangian semi-supervised regularized twin extreme learning machine (Lap-LRTELM). Experimental results on most datasets show that the proposed LRTELM and Lap-LRTELM are competitive in terms of accuracy and efficiency compared to the state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Adaptive Filtering and Machine Learning)
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14 pages, 1252 KiB  
Article
Low-Complexity Constrained Recursive Kernel Risk-Sensitive Loss Algorithm
by Shunling Xiang, Chunzhe Zhao, Zilin Gao and Dongfang Yan
Symmetry 2022, 14(5), 877; https://doi.org/10.3390/sym14050877 - 25 Apr 2022
Viewed by 1146
Abstract
The constrained recursive maximum correntropy criterion (CRMCC) combats the non-Gaussian noise effectively. However, the performance surface of maximum correntropy criterion (MCC) is highly non-convex, resulting in low accuracy. Inspired by the smooth kernel risk-sensitive loss (KRSL), a novel constrained recursive KRSL (CRKRSL) algorithm [...] Read more.
The constrained recursive maximum correntropy criterion (CRMCC) combats the non-Gaussian noise effectively. However, the performance surface of maximum correntropy criterion (MCC) is highly non-convex, resulting in low accuracy. Inspired by the smooth kernel risk-sensitive loss (KRSL), a novel constrained recursive KRSL (CRKRSL) algorithm is proposed, which shows higher filtering accuracy and lower computational complexity than CRMCC. Meanwhile, a modified update strategy is developed to avoid the instability of CRKRSL in the early iterations. By using Isserlis’s theorem to separate the complex symmetric matrix with fourth-moment variables, the mean square stability condition of CRKRSL is derived, and the simulation results validate its advantages. Full article
(This article belongs to the Special Issue Adaptive Filtering and Machine Learning)
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