Article
Automation & Control Systems
Scindhiya Laxmi, S. K. Gupta
Summary: The intuitionistic fuzzy twin support vector machine for multi-categorization is developed in this study, which combines the concepts of structural and empirical risk. Empirical findings show that this method outperforms existing methods on various datasets and has good generalization capacity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Telecommunications
Wanman Li, Xiaozhang Liu, Anli Yan, Jie Yang
Summary: This paper discusses evasion attacks against SVM classification in adversarial machine learning, proposing a defense strategy using vulnerability function and kernel optimization. The defense method proves to be effective on benchmark datasets, improving the robustness of SVM classifiers.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Telecommunications
Wanman Li, Xiaozhang Liu, Anli Yan, Jie Yang
Summary: This paper introduces the evasion attack against SVM classification in the field of adversarial machine learning and proposes an effective defense strategy by optimizing the SVM kernel to enhance the robustness of the classifier.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Geosciences, Multidisciplinary
Mohammad Najafzadeh, Saeid Niazmardi
Summary: This paper introduces a novel Multiple-Kernel Support Vector Regression (MKSVR) algorithm for estimating hard-to-measure water quality parameters, utilizing the Particle Swarm Optimization (PSO) algorithm to solve the optimization problem. Results show that MKSVR provides a more accurate prediction compared to SVR and RFR algorithms.
NATURAL RESOURCES RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Matteo Avolio, Antonio Fuduli
Summary: This paper introduces a novel approach for binary multiple instance learning classification, combining the strengths of SVM and PSVM, aiming to discriminate between positive and negative instances by generating a hyperplane placed in the middle between two parallel hyperplanes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Scindhiya Laxmi, S. K. Gupta, Sumit Kumar
Summary: Intuitionistic fuzzy-based support vector machine is an effective method for multi-category classification problems, which assigns fuzzy score functions to each training point to significantly reduce the impacts of noises and outliers in the dataset.
Article
Computer Science, Artificial Intelligence
Rongda Chen, Zhixia Yang, Junyou Ye
Summary: This article discusses the challenges of using support vector machine (SVM) models in multiview learning and proposes two multiview classifiers, C-MKNSVM and ?-MKNSVM, which overcome the difficulties by using kernel-free techniques. Experimental results show that these classifiers outperform traditional MVL classifiers.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Operations Research & Management Science
Scindhiya Laxmi, S. K. Gupta, Sumit Kumar
Summary: This paper proposes an intuitionistic fuzzy regularized least square twin support vector machine method to improve the TSVM classifier by considering data uncertainties and the importance of patterns in classification. The results show that the proposed method outperforms existing methods in terms of accuracy, computational time, etc., and is also suitable for big datasets.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Chen Ding, Tian-Yi Bao, He-Liang Huang
Summary: The study proposes a quantum-inspired classical algorithm for LS-SVM, utilizing an improved sampling technique for classification. The theoretical analysis indicates that the algorithm can achieve classification with logarithmic runtime for low-rank, low-condition number, and high-dimensional data matrices.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Liming Liu, Maoxiang Chu, Rongfen Gong, Li Zhang
Summary: The improved nonparallel support vector machine (INPSVM) proposed in this article inherits the advantages of nonparallel support vector machine (NPSVM) while also offering incomparable benefits over twin support vector machine (TSVM). INPSVM effectively eliminates noise effects and achieves higher classification accuracy for both linear and nonlinear datasets compared to other algorithms. Experimental results demonstrate the superior efficiency, accuracy, and robustness of INPSVM.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zhizheng Liang, Lei Zhang
Summary: In this paper, novel twin support vector machines (TSVMs) are proposed to handle uncertain data, where each uncertain sample is modeled as a random vector with Gaussian distributions. By deriving an important theorem to simplify the models and using a quasi-Newton optimization algorithm, the optimization problem becomes tractable. Experimental results show that the proposed models outperform some existing algorithms in terms of classification performance, especially for uncertain cross-plane problems.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Bin Gu, Ziran Xiong, Xiang Li, Zhou Zhai, Guansheng Zheng
Summary: This article proposes a new kernel path algorithm (KP nu SVC) to trace the solutions of nu-support vector classification (nu-SVC). It also introduces a new kernel error path (KEP) algorithm that ensures finding the global optimal kernel parameter. Experimental results demonstrate the effectiveness of KP nu SVC and the advantage of using KEP in selecting the optimal kernel parameter.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Arin Chaudhuri, Carol Sadek, Deovrat Kakde, Haoyu Wang, Wenhao Hu, Hansi Jiang, Seunghyun Kong, Yuwei Liao, Sergiy Peredriy
Summary: Support vector data description (SVDD) is a popular anomaly detection technique that requires the use of a Gaussian kernel, with the bandwidth parameter being crucial for optimal performance. This paper introduces a new unsupervised method for selecting the Gaussian kernel bandwidth, utilizing a low-rank representation of the kernel matrix. The new technique is competitive with existing methods for low-dimensional data and excels in handling high-dimensional data.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Jingxuan Pang, Xiaokun Pu, Chunguang Li
Summary: Anomaly detection plays a crucial role in industry for maintaining system safety and ensuring product quality. This article introduces a hybrid algorithm, VQ-OCSVM, which combines vector quantization and OCSVM to address the challenges faced by OCSVM in kernel parameter selection and handling complex data distributions. The proposed method effectively bypasses the kernel parameter selection problem and integrates generative and discriminative learning for better generalization capacity. Experimental results demonstrate the effectiveness and advantages of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Xu Chen, Ya Zhang, Ivor W. Tsang, Yuangang Pan, Jingchao Su
Summary: This article discusses cross-domain recommendation in scenarios where different domains have the same set of users but no overlapping items. Most existing methods focus on shared-user representation, but fail to capture domain-specific features. In this article, an equivalent transformation learner (ETL) is proposed to preserve both domain-specific and overlapped features by modeling the joint distribution of user behaviors across domains.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Engineering, Industrial
Mingxing Li, Daqiang Guo, Ming Li, Ting Qu, George Q. Huang
Summary: The widespread adoption of Industry 4.0 technologies is revolutionising manufacturing operations. This paper introduces a novel concept of operations twins (OT) for achieving synchronisation between production and intralogistics (PiL) through the use of Industry 4.0 technologies and innovative operations management strategies.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Xiaowei Zhou, Ivor W. Tsang, Jie Yin
Summary: Deep Neural Networks have achieved great success in classification tasks, but they are vulnerable to adversarial attacks. Adversarial training is an effective strategy to improve the robustness of DNN models, but existing methods fail to generalize well to standard test data. To achieve a better trade-off between standard accuracy and adversarial robustness, a novel adversarial training framework called LADDER is proposed, which generates high-quality adversarial examples through perturbations on latent features.
Article
Computer Science, Artificial Intelligence
Yiming Xu, Lin Chen, Lixin Duan, Ivor W. Tsang, Jiebo Luo
Summary: This article studies the problem of open set domain adaptation and proposes a method that performs soft rejection of unknown target classes and simultaneously matches the source and target domains. Extensive experiments on three standard datasets validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiangchao Yao, Bo Han, Zhihan Zhou, Ya Zhang, Ivor W. Tsang
Summary: Learning with noisy labels is important in the Big Data era to save costs. Previous noise-transition-based methods achieved good performance but relied on impractical anchor sets. Our approach introduces a Bayesian framework for parameterizing the noise transition and solves the problem of ill-posed stochastic learning in back-propagation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Bing Li, Wei Cui, Le Zhang, Ce Zhu, Wei Wang, Ivor W. Tsang, Joey Tianyi Zhou
Summary: Time series analysis is crucial in various fields such as economics, finance, and surveillance. However, traditional Transformer models have limitations in representing nuanced patterns in time series data. To overcome these challenges, we propose a novel Transformer architecture called DifFormer, which incorporates a multi-resolutional differencing mechanism. DifFormer outperforms existing models in classification, regression, and forecasting tasks, while also exhibiting efficiency and lower time consumption.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Defu Liu, Wen Li, Lixin Duan, Ivor W. Tsang, Guowu Yang
Summary: Deep models have achieved impressive performance in various visual recognition tasks, but their generalization ability is compromised by noisy labels. This paper presents a dynamic label learning algorithm that allows the use of different loss functions for classification in the presence of label noise, ensuring that the search for the optimal classifier of noise-free samples is not hindered by label noise.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yongshun Gong, Zhibin Li, Wei Liu, Xiankai Lu, Xinwang Liu, Ivor W. W. Tsang, Yilong Yin
Summary: Many real-world problems involve data with missing values, which can hinder learning achievements. Existing methods use a universal model for all incomplete data, resulting in suboptimal models for each missingness pattern. This paper proposes a general model that can adjust to different missingness patterns, minimizing competition between data. The model is based on observable features and does not rely on data imputation, and a low-rank constraint is introduced to improve generalization ability.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang
Summary: Multi-variate time series (MTS) data is a common type of data abstraction in the real world, generated from a hybrid dynamical system. MTS data can be categorized into spatial and temporal attributes, and can be analyzed from the spatial view or temporal view. A novel multi-view multi-task (MVMT) learning framework is proposed to extract hidden MVMT information from MTS data while predicting. The framework improves effectiveness and efficiency of canonical architectures according to extensive experiments on three datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Shudong Huang, Ivor W. W. Tsang, Zenglin Xu, Jiancheng Lv
Summary: Multi-view clustering aims to reveal correlations between different input modalities in an unsupervised way. This paper proposes a novel model that learns a robust structured similarity graph and performs multi-view clustering simultaneously. The similarity graph is adaptively learned based on a latent representation that is invulnerable to noise and outliers. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed model.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jing Li, Yuangang Pan, Ivor W. Tsang
Summary: This article proposes a dual mechanism called adaptive sharpening (ADS) to minimize prediction uncertainty in semi-supervised learning. ADS applies a soft-threshold to mask out uncertain and negligible predictions, and sharpens the informed ones to distill certain predictions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaofeng Cao, Ivor W. Tsang
Summary: Machine teaching is a reverse problem of machine learning, aiming to guide the student towards its target hypothesis using known learning parameters. Previous studies focused on balancing teaching risk and cost to find the best teaching examples. However, when the student doesn't disclose any cue of the learning parameters, the optimization solver becomes ineffective. This article presents a distribution matching-based machine teaching strategy that iteratively shrinks teaching cost to eliminate boundary perturbations, providing an effective solution.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuhao Liu, Qing Guo, Lan Fu, Zhanghan Ke, Ke Xu, Wei Feng, Ivor W. Tsang, Rynson W. H. Lau
Summary: In this paper, a novel structure-informed shadow removal network (StructNet) is proposed to address the problem of shadow remnants in existing deep learning-based methods. StructNet reconstructs the structure information of the input image without shadows and uses it to guide the image-level shadow removal. Two main modules, MSFE and MFRA, are developed to extract image structural features and regularize feature consistency. Additionally, an extension called MStructNet is proposed to exploit multi-level structure information and improve shadow removal performance.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhihao Hu, Dong Xu
Summary: In this paper, we propose a combination of complexity-guided slimmable decoder (cgSlimDecoder) and skip-adaptive entropy coding (SaEC) for efficient deep video compression. The cgSlimDecoder automatically determines the optimal channel width for each slimmable convolution layer and allocates the optimal number of parameters for different modules, supporting multiple complexity levels. The SaEC further speeds up the decoding process by skipping the entropy coding for well-predicted elements. Experimental results demonstrate that the proposed methods significantly improve coding efficiency with minimal performance drop.
2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2023)
Article
Computer Science, Artificial Intelligence
Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang
Summary: This paper addresses the problem of data-efficient learning from scratch in scenarios where data or labels are expensive to collect. It proposes the MHEAL algorithm based on active learning on homeomorphic tubes of spherical manifolds, and provides comprehensive theoretical guarantees. Empirical results demonstrate the effectiveness of MHEAL in various applications for data-efficient learning.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)