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multi-view learning with enhanced multi-weight vector projection support vector machine

NEURAL NETWORKS 2025年 185卷 107180页

作者： Yan, Xin Wang, Shuaixing Chen, Huina Zhu, Hongmiao Shanghai Univ Int Business & Econ Sch Stat & Informat Shanghai 201620 Peoples R China Shanghai Univ Int Business & Econ Sch Management Shanghai 201620 Peoples R China

multi-view learning aims on learning from the data represented by multiple distinct feature sets. Various multi-view support vector machine methods have been successfully applied to classification tasks. However, the existed methods often face the problems of long processing time or weak generalization on some complex datasets. In this paper, two multi-view enhanced multi-weight vector projection support vector machine models are proposed. One is a ratio form of multi-view enhanced multi-weight vector projection support vector machine (R-MvEMV), while the other is a difference form (D-MvEMV). Instead of searching for specific classification hyperplanes, each proposed model tries to generate two projection matrices composed of set of projection vectors for each view. A co-regularization term is added to maximize the consistency of different views. R-MvEMV and D-MvEMV can be simplified to two generalized eigenvalue problems and two eigenvalue problems, respectively. The optimal weight vector projections are the eigenvectors corresponding to the smallest eigenvalues. Some numerical tests are conducted to compare the proposed methods with the other state-of-art multi-view support vector machine methods. The numerical results show the better classification performance and higher efficiency of the proposed methods.

关键词： Support vector machine multi-view learning Classification Eigenvalue problem

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multi-view learning based on product and process metrics for software defect prediction

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APPLIED INTELLIGENCE 2025年第6期55卷 1-20页

作者： Sun, Ying Wu, Fei Wu, Di Jing, Xiao-Yuan Sun, Yanfei Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China NanJing Pharmaceut Co Ltd Digital Innovat Dept Nanjing 210012 Peoples R China Nanjing Univ Posts & Telecommun Sch Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Res Ctr HPC & Intelligent Proc Nanjing 210003 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Software defect prediction plays a crucial role as a quality assurance technology in software development. The software metrics are associated with the software quality and are vital for prediction models. Most existing defect prediction methods build the prediction model ignoring the complementary information between these two kinds of metrics. In this work, we intend to jointly leverage these two kinds of metrics. For a software instance, we regard the product metrics and the process metrics as its two views. We model the problem of discriminative feature learning from these two kinds of metrics as the problem of multi-view learning. However, it is a challenging task to construct an effective prediction model based on both product and process metrics due to the heterogeneity in data of product and process metrics, and the defect data often has class imbalance characteristic. How to explore the discriminant both inter-view and intra-view effectively has not been well studied. These characteristics make it challenging to construct an effective prediction model. In this paper, we propose a Deep multi-view Defect Prediction (DMDP) approach, which can predict software defect based on both product and process metrics. We design a neural network with two sub-network branches, which are enforced to share the weights in the last output layer, to map the data from different views to a common space. To guide the training of networks, we design the loss function including the discrepancy loss, discrimination loss and classification loss, which further promotes the distribution consistency across views, makes full use of label information to obtain the discriminative representations, and utilizes the complementarity information for prediction. To alleviate the class imbalance problem, we design a dynamic sampling strategy for dealing with class-imbalanced data. Comprehensive experiments are conducted on 15 projects from three widely used defect datasets. The experimental resul

关键词： Software defect prediction Product metrics Process metrics multi-view learning Deep learning

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multi-view learning for camouflaged object detection with PVTv2

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INTERNATIONAL JOURNAL OF multiMEDIA INFORMATION RETRIEVAL 2025年第2期14卷 1-11页

作者： Yan, Pu Ruan, Kang Wang, Lili Zhao, Yang Wang, Xu Anhui Jianzhu Univ Sch Elect & Informat Engn Hefei 230601 Anhui Peoples R China

Recently, with the continuous development in the field of camouflaged object detection (COD), effectively separating objects highly similar to the background has become a focal point of research. Due to the high similarity between camouflaged objects and backgrounds, traditional single visual branch often perform poorly in such scenarios. To address this issue, we propose a multi-view learning detection network based on the Pyramid Vision Transformer, named multi-view learning for Camouflaged Object Detection with PVTv2 (MVLNet). By utilizing the information from RGB and noise views, our method can provide a more comprehensive description of the relationship between objects and backgrounds to improve the accuracy and robustness for COD. Inspired by human visual attention during observation, we design a Global Context Aggregation Module by using a U-shaped structure and progressively increasing dilation rates to simulate the human behavior of zooming in and out. Extensive experiments demonstrate that the proposed MVLNet outperforms 23 other representative models on three public datasets.

关键词： Camouflaged object detection multi-view learning Global context aggregation module Pyramid vision transformer

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Missing data as augmentation in the Earth Observation domain: A multi-view learning approach

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NEUROCOMPUTING 2025年 638卷

作者： Mena, Francisco Arenas, Diego Dengel, Andreas Univ Kaiserslautern Landau Comp Sci Gottlieb Daimler Str D-67663 Kaiserslautern Germany German Res Ctr Artificial Intelligence SDS Trippstadter Str 122 D-67663 Kaiserslautern Germany

multi-view learning (MVL) leverages multiple sources or views of data to enhance machine learning performance and robustness. This approach has been successfully used in the Earth Observation (EO) domain, where views have a heterogeneous nature and can be affected by missing data. Despite the negative that missing data has on model predictions, the ML literature has used it as an augmentation technique improve model generalization, like masking the input data. Inspired by this, we introduce novel methods applications tailored to MVL with missing views. Our methods integrate the combination of a set to simulate combinations of missing views as different training samples. Instead of replacing missing data with a numerical value, we use dynamic merge functions, like average, and more complex ones like Transformer. This the MVL model to entirely ignore the missing views, enhancing its predictive robustness. We experiment four EO datasets with temporal and static views, including state-of-the-art methods from the EO domain. results indicate that our methods improve model robustness under conditions of moderate missingness, improve the predictive performance when all views are present. The proposed methods offer a single adaptive solution to operate effectively with any combination of available views.

关键词： multi-view learning Earth Observation Missing data Data augmentation Robustness

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Consensus and diversity-fusion partial-view-shared multi-view learning

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NEUROCOMPUTING 2025年 611卷

作者： Teng, Luyao Zheng, Zefeng Guangzhou Panyu Polytech Sch Informat Engn Guangzhou 511483 Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

Due to the multi-perspective of data, multi-view learning (MVL) is usually employed. Although existing MVL approaches gain fruitful achievements, they may neglect to learn (a) partial-view-shared knowledge between views;and (b) differences across views. Consequently, inadequate complementary knowledge and weak discriminability may be gained. To address the above problems, Consensus and Diversity-fusion Partial- view-shared multi-view learning (CDPMVL) is proposed, which includes two components: (a) Consensus, Partial-view-shared and Specific Component learning (CPSCL) that partitions the samples into consensual, partial-view-shared, and specific parts, and learns the consensual, partial-view-shared, and specific knowledge of views;and (b) Diversity-fusion Partial-view-shared Knowledge Enhancement (DPKE) that imposes a diversity constraint on partial-view-shared parts and employs a heuristic-based auto-weighting mechanism to highlight the differences among views. By CDPMVL, more complementary relationships between and across views are explored, and the discriminability of the model is enhanced. Extensive experiments performed with eleven algorithms on nine datasets verify the superiority of CDPMVL, which indicates that the incorporation of partial- view-shared knowledge indeed enhances the complementary ability of views.

关键词： multi-view learning Consistency and inconsistency Partial-view-shared knowledge Re-weighting

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A review on multi-view learning

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FRONTIERS OF COMPUTER SCIENCE 2025年第7期19卷 197334-197334页

作者： Yu, Zhiwen Dong, Ziyang Yu, Chenchen Yang, Kaixiang Fan, Ziwei Chen, C. L. Philip South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Shenzhen 518066 Peoples R China

multi-view learning is an emerging field that aims to enhance learning performance by leveraging multiple views or sources of data across various domains. By integrating information from diverse perspectives, multi-view learning methods effectively enhance accuracy, robustness, and generalization capabilities. The existing research on multi-view learning can be broadly categorized into four groups in the survey based on the tasks it encompasses, namely multi-view classification approaches, multi-view semi-supervised classification approaches, multi-view clustering approaches, and multi-view semi-supervised clustering approaches. Despite its potential advantages, multi-view learning poses several challenges, including view inconsistency, view complementarity, optimal view fusion, the curse of dimensionality, scalability, limited labels, and generalization across domains. Nevertheless, these challenges have not discouraged researchers from exploring the potential of multiview learning. It continues to be an active and promising research area, capable of effectively addressing complex real-world problems.

关键词： multi-view learning multi-view clustering ensemble learning semi-supervised learning

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multi-scale graph diffusion convolutional network for multi-view learning

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ARTIFICIAL INTELLIGENCE REview 2025年第6期58卷 1-23页

作者： Wang, Shiping Li, Jiacheng Chen, Yuhong Wu, Zhihao Huang, Aiping Zhang, Le Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610054 Peoples R China

multi-view learning has attracted considerable attention owing to its capability to learn more comprehensive representations. Although graph convolutional networks have achieved encouraging results in multi-view research, their limitation to considering only nearest neighbors results in the decrease on the ability to obtain high-order information. Many existing methods acquire high-order correlation by stacking multiple layers onto the model, yet they could lead to the issue of over-smoothing. In this paper, we propose a framework termed multi-scale graph diffusion convolutional network, which aims to gather comprehensive higher-order information without stacking multiple convolutional layers. Specifically, in order to better expand the receptive field of the node and reduce the parameter complexity, the proposed framework utilizes a contractive mapping to transform features from multiple views on decoupled propagation rules. Our framework introduces a multi-scale graph-based diffusion mechanism to adaptively extract the abundant high-order knowledge embedded within multi-scale graphs. Experiments show that the proposed method outperforms other state-of-the-art methods in terms of multi-view semi-supervised classification.

关键词： Graph diffusion multi-scale fusion Graph convolutional network multi-view learning Semi-supervised classification.

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Trust EEG epileptic seizure detection via evidential multi-view learning ☆

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INFORMATION SCIENCES 2025年 694卷

作者： Liu, Ying Xu, Cai Wen, Ziqi Dong, Yansong Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Beijing Sunwise Informat Technol Ltd Beijing 100190 Peoples R China

Epilepsy is one of the most common neurological disease in the world. Researchers focus on automatic electroencephalogram (EEG) seizure detection methods and achieve remarkable detection accuracy. However, there still exist highly uncertain decisions, which may be caused by the noise signals such as ocular artifacts. Considering the misclassification of an epileptic patient as non-seizure could have severe consequences, we point out that the automatic EEG seizure detection methods should explicitly provide the reliability of the detection. To achieve this goal, we propose an Evidential multi-view learning (EML) method, which reduces noises by multiple feature extraction methods and make trusted decision accordingly. EML constructs initial multi-view features to capture the diverse information of EEG signals. For each view, EML establishes deep neural networks to learn high-level features and view-specific evidence of each category, which could be termed as the amount of support to each category collected from data. By dynamically fusing different views at the evidence level, EML makes reliable prediction accordingly (strong evidence indicates high prediction reliability). We empirically evaluate EML on a public EEG Epileptic Seizure Detection dataset (CHB_MIT). Experiments show the superiority of EML over state-of-the-art baseline methods. We will release the code in https://***/ Wednesque/Trust-EEG-Epileptic-Seizure-Detection-via-Evidential-multi-view-learning.

关键词： multi-view learning Trustworthy deep learning Epileptic seizure detection Electroencephalogram

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TSCMamba: Mamba meets multi-view learning for time series classification

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INFORMATION FUSION 2025年 120卷 103079页

作者： Ahamed, Atik Cheng, Qiang Univ Kentucky Dept Comp Sci Lexington KY 40506 USA Univ Kentucky Inst Biomed Informat Lexington KY 40506 USA

multivariate time series classification (TSC) is critical for various applications in fields such as healthcare and finance. While various approaches for TSC have been explored, important properties of time series, such as shift equivariance and inversion invariance, are largely underexplored by existing works. To fill this gap, we propose a novel multi-view approach to capture patterns with properties like shift equivariance. Our method integrates diverse features, including spectral, temporal, local, and global features, to obtain rich, complementary contexts for TSC. We use continuous wavelet transform to capture time-frequency features that remain consistent even when the input is shifted in time. These features are fused with temporal convolutional or multilayer perceptron features to provide complex local and global contextual information. We utilize the Mamba state space model for efficient and scalable sequence modeling and capturing long-range dependencies in time series. Moreover, we introduce a new scanning scheme for Mamba, called tango scanning, to effectively model sequence relationships and leverage inversion invariance, thereby enhancing our model's generalization and robustness. Experiments on two sets of benchmark datasets (10+20 datasets) demonstrate our approach's effectiveness, achieving average accuracy improvements of 4.01-6.45% and 7.93% respectively, over leading TSC models such as TimesNet and TSLANet.

关键词： Time series classification Deep learning State-space-machine multi-view learning

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An integrating multidimensional features method based on multi-view learning for intelligent fault diagnosis of rolling bearings

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ENGINEERING COMPUTATIONS 2025年第4期42卷 1502-1524页

作者： Wang, Min Liu, Weixia Ning, Jida Yu, Shihang Tang, Shikai Li, Jiaqi Tiangong Univ Sch Life Sci Tianjin Peoples R China Tiangong Univ Sch Control Sci & Engn Tianjin Peoples R China Unit 32372 Peoples Liberat Army Beijing Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao Peoples R China

PurposeThe purpose of this study is to improve the accuracy and generalization ability of intelligent fault diagnosis models for rolling bearings under varying operating conditions. By integrating multidimensional features through multi-view learning (MVL) and utilizing Mamba feature fusion, the method aims to address the challenge of data distribution differences that reduce diagnostic accuracy when working conditions change. The approach also incorporates domain adaptation techniques to align source and target domain data, ensuring robust and accurate fault detection. This work seeks to enhance fault diagnosis performance, reduce maintenance costs and ensure operational continuity in industrial ***/methodology/approachThis paper proposes an integrating multidimensional feature method based on multi-view learning (IMDF-MVL) for intelligent fault diagnosis of rolling bearings. MVL is used to capture multidimensional fault features, while Mamba feature fusion combines features from different views to enhance the model's generalization ability. Domain adaptation is applied to align data distributions between source and target domains. Experimental validation is conducted by comparing IMDF-MVL with state-of-the-art methods, demonstrating its superior diagnostic accuracy and robustness under varying conditions. The proposed approach aims to provide an effective solution for real-world industrial fault detection *** findings of this study demonstrate that the proposed IMDF-MVL method significantly outperforms existing fault diagnosis models, such as DCTLN, NCNN, InDo-DDM, GMVTDA and RTDGN, in both source and target domain datasets. On the source domain, IMDF-MVL achieves an average diagnostic accuracy of 99.98 and 99.89%, highlighting its high efficiency and stability. In target domain transfer experiments, even without target domain fine-tuning, the method achieves diagnostic accuracies of 93.71 and 63.40%, indicating its robustness

关键词： Intelligent fault diagnosis multi-view learning multidimensional features Mamba feature fusion Domain distribution alignment

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