Article
Computer Science, Information Systems
Chenglong Zhang, Bingbing Jiang, Zidong Wang, Jie Yang, Yangfeng Lu, Xingyu Wu, Weiguo Sheng
Summary: In this paper, an efficient multi-view feature selection method (EMSFS) is proposed to address the issues in multi-view semi-supervised feature selection. EMSFS combines graph learning, label propagation, and multi-view feature selection within a unified framework. The method can adaptively learn a graph and exploit the similarity structure to enhance the reliability of the graph. It also achieves high computational efficiency.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Esra Adiyeke, Mustafa Gokce Baydogan
Summary: This paper introduces alternative semi-supervised tree-based strategies that are robust to scale differences both in terms of feature and target variables. Proposing the use of a scale-invariant proximity measure by means of tree-based ensembles to preserve the original characteristics of the data, the paper updates the classical tree derivation procedure to a multi-criteria form to resolve scale inconsistencies.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Hui Zhou, Maoguo Gong, Shanfeng Wang, Yuan Gao, Zhongying Zhao
Summary: Graph contrastive learning (GCL) aims to generate supervision information by transforming graph data itself, and it has become a focus of graph research recently. However, most GCL methods are unsupervised and struggle with balancing multi-view graph information. To address this, we propose a semi-supervised multi-view graph contrastive learning (SMGCL) framework for graph classification. The framework captures comparative relations between label-independent and label-dependent node pairs across different views and incorporates a label augmentation module and a shared decoder module to enhance discriminative representations and extract underlying relationships between representations and graph topology. Experimental results demonstrate the superiority of our proposed framework for graph classification tasks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Xiaoli Wang, Liyong Fu, Yudong Zhang, Yongli Wang, Zechao Li
Summary: MMatch is a new semi-supervised discriminative representation learning method for multi-view classification. It learns view-specific representations and class probabilities, integrates multiple views' information into a global representation, and regularizes the structure of view-specific representations with pseudo labels.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Geng Yang, Qin Li, Yu Yun, Yu Lei, Jane You
Summary: In this study, a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach is proposed to fully utilize the high-order geometrical structure and complementary information in multi-view data, leading to improved clustering results.
Article
Computer Science, Artificial Intelligence
Aiping Huang, Zheng Wang, Yannan Zheng, Tiesong Zhao, Chia-Wen Lin
Summary: This study proposes an embedding regularizer learning scheme for multi-view semi-supervised classification. By integrating diversity, sparsity, and consensus, the framework effectively handles limited labeled multi-view data and demonstrates its effectiveness and superiority through extensive experimental results.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Information Systems
Xiao Shen, Haofeng Zhang, Lunbo Li, Wankou Yang, Li Liu
Summary: This study proposes a novel multi-view graph cross-modal hashing (MGCH) method for generating hash codes in a semi-supervised manner. Unlike conventional graph-based hashing methods, MGCH only employs multi-view graphs as learning assistance and demonstrates superiority in cross-modal hashing tasks.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Yongqiang Tang, Yuan Xie, Chenyang Zhang, Wensheng Zhang
Summary: In this study, a novel method is proposed to incorporate weakly supervised information into multi-view subspace clustering, showing better performance compared to traditional methods. By introducing a regularization method that integrates must-link, cannot-link, and normalization constraints into a unified formulation, a flexible framework is provided for subspace clustering tasks.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Artificial Intelligence
Wei Guo, Zhe Wang, Wenli Du
Summary: The construction of a high-quality multi-view consensus graph is crucial for graph-based semi-supervised multi-view learning methods. Existing methods often produce contaminated graphs that cannot reveal the underlying manifold structure of samples. Additionally, traditional methods fail to explore the diverse structures of multi-view features, resulting in suboptimal graphs.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
N. Ziraki, A. Bosaghzadeh, F. Dornaika, Z. Ibrahim, N. Barrena
Summary: Graphs play a crucial role in the performance of graph-based semi-supervised learning methods, and their construction should be carefully considered. This letter focuses on graph-based semi-supervised learning with multiple views for the data, addressing the missing concept of data smoothness in graph construction. By merging data smoothness and label smoothness, and performing label fitness and projection matrix calculation, this approach effectively improves the efficiency and performance of semi-supervised classification compared to single feature methods and other fusion algorithms. Experimental results with image databases demonstrate the superiority of this approach.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Xiaodong Jia, Xiao-Yuan Jing, Xiaoke Zhu, Songcan Chen, Bo Du, Ziyun Cai, Zhenyu He, Dong Yue
Summary: This paper proposes a semi-supervised multi-view deep discriminant representation learning approach, which utilizes the consensus and complementary properties of multi-view data to learn shared and specific representations, while reducing redundancy through orthogonality and adversarial similarity constraints. Experimental results demonstrate its effectiveness in typical multi-view learning tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Remote Sensing
Bing Liu, Xibing Zuo, Anzhu Yu, Yifan Sun, Ruirui Wang
Summary: This study proposes a semi-supervised deep-learning method based on multi-view consistency to improve the classification accuracy of hyperspectral images using a few labelled samples. The method builds a classifier based on a residual network and introduces an attention mechanism to enhance classification performance. The unsupervised loss function is used to train the model and fully utilize unlabelled samples for improved accuracy.
REMOTE SENSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Bin Zhang, Qianyao Qiang, Fei Wang, Feiping Nie
Summary: This study combines an anchor-based approach with multi-view semi-supervised learning to propose a new method called fast multi-view SSL (FMSSL). By learning a graph model, FMSSL addresses the challenges in multi-view SSL, improving performance while reducing computational complexity. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Kaixuan Yao, Jiye Liang, Jianqing Liang, Ming Li, Feilong Cao
Summary: This paper proposes a novel framework based on multi-view graph convolutional networks, which enhances the expressive power and flexibility of graph data modeling by incorporating multiple trusted topology views and attention-based feature aggregation strategy. The experimental results demonstrate the state-of-the-art accuracies of this framework on multiple datasets and its advantage in handling uncertainty issues.
ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yuyuan Yu, Guoxu Zhou, Haonan Huang, Shengli Xie, Qibin Zhao
Summary: This paper proposes a semi-supervised label-driven auto-weighted strategy to evaluate the importance of views in multi-view learning. Based on this strategy, a transductive semi-supervised auto-weighted multi-view classification model is proposed. Experimental results show that the proposed method achieves promising classification accuracy at a lower computational cost and can distinguish view importance more accurately.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Sang-Bing Tsai, Xusen Cheng, Yanwu Yang, Jason Xiong, Alex Zarifis
Summary: This article structurally concludes the methods proposed and evidenced to develop digital entrepreneurship from a socio-technical perspective. The technology itself and the process of utilization should be carefully considered. From a social perspective, fulfilling the needs of customers in social interaction and nurturing characteristics and social skills for the digital work environment are crucial.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xiaochang Fang, Hongchen Wu, Jing Jing, Yihong Meng, Bing Yu, Hongzhu Yu, Huaxiang Zhang
Summary: This study proposes a novel fake news detection framework, utilizing news semantic environment perception (NSEP) to identify fake news content. The framework consists of steps such as dividing the semantic environment into macro and micro levels, applying graph convolutional networks, and utilizing multihead attention. Empirical experiments show that the NSEP framework achieves high accuracy in detecting Chinese fake news, outperforming other baseline methods and highlighting the importance of both micro and macro semantic environments in early detection of fake news.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xudong Sun, Alladoumbaye Ngueilbaye, Kaijing Luo, Yongda Cai, Dingming Wu, Joshua Zhexue Huang
Summary: This paper proposes a scalable distributed frequent itemset mining (ScaDistFIM) algorithm to address the data scalability and flexibility issues in basket analysis in the big data era. Experiment results demonstrate that the ScaDistFIM algorithm is more efficient compared to the Spark FP-Growth algorithm.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Boxu Guan, Xinhua Zhu, Shangbo Yuan
Summary: This paper aims to improve the interpretability of machine reading comprehension models by utilizing the pre-trained T5 model for evidence inference. They propose an interpretable reading comprehension model based on T5, which is trained on a more accurate evidence corpus and can infer precise interpretations for answers. Experimental results show that their model outperforms the baseline BERT model on the SQuAD1.1 task.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang
Summary: In this study, we propose a data augmentation-based semantic text matching model called STMAP. By using Gaussian noise and noise mask signal for data augmentation, as well as employing an adaptive optimization network for training target optimization, our model achieves good performance in few-shot learning and semantic deviation problems.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jiahao Yang, Shuo Feng, Wenkai Zhang, Ming Zhang, Jun Zhou, Pengyuan Zhang
Summary: To pursue profit from stock markets, researchers utilize deep learning methods to forecast asset price movements. However, there are two issues in current research, the discrepancy between forecasting results and profits, and heavy reliance on prior knowledge. To address these issues, researchers propose a novel optimization objective and modeling method, and conduct experiments to validate their approach.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Summary: This study provides an accurate analysis of technology development in the field of Natural Language Processing (NLP) from an entity-centric perspective. The findings indicate an increase in the average number of entities per paper, with pre-trained language models becoming mainstream and the impact of Wikipedia dataset and BLEU metric continuing to rise. There has been a surge in popularity for new high-impact technologies in recent years, with researchers accepting them at an unprecedented speed.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Davide Buscaldi, Danilo Dessi, Enrico Motta, Marco Murgia, Francesco Osborne, Diego Reforgiato Recupero
Summary: In scientific papers, citing other articles is a common practice to support claims and provide evidence. This paper proposes two automatic methods using Transformer models to address citation placement, and achieves significant improvements in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Baozhuang Niu, Lingfeng Wang, Xinhu Yu, Beibei Feng
Summary: This paper examines whether the incumbent brand should adopt digital technology to forecast demand and adjust order decisions in the face of soaring demand for medical supply caused by frequent outbreaks of regional COVID-19 epidemic. The study finds that digital transformation can lead to a triple-win situation among the incumbent brand, social welfare, and consumer surplus, as well as bring benefits to the manufacturer. Furthermore, the research provides insights for firms' digital entrepreneurship decisions through theoretical optimization and data processing/policy simulation.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xueyang Qin, Lishang Li, Fei Hao, Meiling Ge, Guangyao Pang
Summary: Image-text retrieval is important in connecting vision and language. This paper proposes a method that utilizes prior knowledge to enhance feature representations and optimize network training for better retrieval results.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Review
Computer Science, Information Systems
Gang Ren, Lei Diao, Fanjia Guo, Taeho Hong
Summary: This paper proposes a novel approach for predicting the helpfulness of reviews by utilizing both textual and image features. The proposed method considers the correlation between features through self-attention and co-attention mechanisms, and fuses multi-modal features for prediction. Experimental results demonstrate the superior performance of the proposed method compared to benchmark methods.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhongquan Jian, Jiajian Li, Qingqiang Wu, Junfeng Yao
Summary: Aspect-Level Sentiment Classification (ALSC) is a crucial challenge in Natural Language Processing (NLP). Most existing methods fail to consider the correlations between different instances, leading to a lack of global viewpoint. To address this issue, we propose a Retrieval Contrastive Learning (RCL) framework that extracts intrinsic knowledge across instances for improved instance representation. Experimental results demonstrate that training ALSC models with RCL leads to substantial performance improvements.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Ying Hu, Yanping Chen, Ruizhang Huang, Yongbin Qin, Qinghua Zheng
Summary: Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. This study proposes a hierarchical convolutional model to address the semantic overlapping and data imbalance problems. The model encodes both local contextual features and global semantic dependencies, enhancing the discriminability of the neural network for biomedical relation extraction.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhou Yang, Yucai Pang, Xuehong Li, Qian Li, Shihong Wei, Rong Wang, Yunpeng Xiao
Summary: This study proposes a rumor detection model based on topic audiolization, which transforms the topic space into audio-like signals. Experimental results show that the model achieves significant performance improvements in rumor identification.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Alistair Moffat
Summary: This paper proposes the buying power metric for assessing the quality of product rankings on e-commerce sites. It discusses the relationship between the buying power metric and user reactions, and introduces an alternative product ranking effectiveness metric.
INFORMATION PROCESSING & MANAGEMENT
(2024)