Article
Computer Science, Information Systems
Yingxu Wang, Long Chen, Jin Zhou, Tianjun Li, Yufeng Yu
Summary: This paper proposes a new pairwise constraints-based semi-supervised fuzzy clustering method with multi-manifold regularization (MMRFCM), which can overcome the deficiencies of current methods and achieve excellent clustering results.
INFORMATION SCIENCES
(2023)
Review
Computer Science, Information Systems
Jianghui Cai, Jing Hao, Haifeng Yang, Xujun Zhao, Yuqing Yang
Summary: Semi-supervised clustering (SSC) is a technique that integrates semi-supervised learning and clustering analysis to improve clustering performance by incorporating prior information. This paper provides a comprehensive review of SSC, organized into different categories and discusses their performance, suitable scenarios, and ways to add supervising information. It also summarizes successful applications of SSC in various fields and provides application caveats and development trends. This review and analysis of SSC can benefit researchers in providing an overall understanding, research topics, and analysis of existing methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Zhen Wang, Shan-Shan Wang, Lan Bai, Wen-Si Wang, Yuan-Hai Shao
Summary: This article proposes a semisupervised fuzzy clustering method with fuzzy pairwise constraints, which can represent more complex relationships between samples and avoid eliminating fuzzy characteristics. The method solves a nonconvex optimization problem using a modified expectation-maximization algorithm and diagonal block coordinate descent algorithm, and is extended to different metric spaces. Experimental results demonstrate the superior performance of this method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Phung The Huan, Pham Huy Thong, Tran Manh Tuan, Dang Trong Hop, Vu Duc Thai, Nguyen Hai Minh, Nguyen Long Giang, Le Hoang Son
Summary: Data partition with high confidence has been a major focus of researchers in Soft Computing for many years. Safe semi-supervised fuzzy clustering has been widely used to tackle this problem, but it often takes a long time and may produce unreasonable results. In this research, a new algorithm called TS3FCM is proposed to address the computational time issue by finding trusted labeled data and performing semi-supervised fuzzy clustering in isolated processes. The key contributions of the paper include a new objective function and a new semi-supervised fuzzy clustering model. Experimental results show that TS3FCM runs faster while maintaining reasonable clustering quality compared to the related algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Weizhong Yu, Liyin Xing, Feiping Nie, Xuelong Li
Summary: Semi supervised clustering algorithms with limited prior information have attracted attention recently. Graph-based algorithms have been proposed due to their natural suitability for pairwise constraints. However, most graph-based approaches have high time complexity and suffer from constraint violation. In this paper, we propose a new non-parametric model that can directly obtain the indicator matrix and handle constraint violation in an efficient and effective way.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Genetics & Heredity
Zeyuan Wang, Hong Gu, Minghui Zhao, Dan Li, Jia Wang
Summary: A new multi-objective semi-supervised clustering algorithm is proposed for unlabeled gene expression data, which incorporates multi-source constraints and constraint selection to improve clustering performance. The algorithm achieves superior performance by applying the improved constraint violation penalty weight and synergistic optimization of pairwise constraints to reduce the negative influence of noisy constraints.
FRONTIERS IN GENETICS
(2023)
Article
Chemistry, Multidisciplinary
Mona Suliman Alzuhair, Mohamed Maher Ben Ismail, Ouiem Bchir
Summary: In this paper, a novel semi-supervised deep clustering approach named SC-DEC is proposed to address the limitations exhibited by existing semi-supervised clustering approaches. The proposed approach leverages a deep neural network architecture to generate fuzzy membership degrees that better reflect the true partition of the data. Experimental results show that utilizing minimal previous knowledge about the data can improve the overall clustering performance.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Zhanhu Zhang, Xia Yu, Rui Tao, Xinyu Zhang, Hongru Li, Jingyi Lu, Jian Zhou
Summary: Cluster analysis is an effective method in data mining for discovering natural structures from different perspectives of data objects. The performance of unsupervised clustering algorithms has been improved by the emergence of semi-supervised clustering techniques. Clustering with guidance information, a variant of clustering method, uses pairwise constraints based on background knowledge to increase interpretability of results but suffers from constraint conflict problem. This paper proposes a novel pairwise constraint representation called knowledge augmentation-based soft constraints and a Soft Constraints Kmeans (SCop-Kmeans) method to resolve constraint conflicts.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Fariba Salehi, Mohammad Reza Keyvanpour, Arash Sharifi
Summary: The study introduced a new algorithm called SMKFC-ER, which focuses on external knowledge related to labeled data and combines entropy and relative entropy for semi-supervised multiple kernel fuzzy clustering. The use of relative entropy and entropy helps the semi-supervised section share more consistent concepts, control cluster fuzziness, and regularly determine kernel weights for the unsupervised section.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Tran Manh Tuan, Mai Dinh Sinh, Tran Dinh Khang, Phung The Huan, Tran Thi Ngan, Nguyen Long Giang, Vu Duc Thai
Summary: This paper proposes an improvement to semi-supervised fuzzy clustering methods by using multiple fuzzifiers. The proposed models show higher performance compared to related models, as evaluated on different datasets.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Yalin Wang, Jiangfeng Zou, Kai Wang, Chenliang Liu, Xiaofeng Yuan
Summary: Semi-supervised deep clustering methods have received much attention for their impressive performance in end-to-end clustering task. However, obtaining satisfactory clustering results is challenging due to the strong and incorrect influence of overlapping samples in industrial text datasets. Existing methods incorporate prior knowledge using pairwise constraints or class labels, which often ignore the correlation between these two types of supervision information and lead to weak-supervised constraints or incorrect strong-supervised label guidance. To address these problems, we propose a novel semi-supervised method called PCSA-DEC based on pairwise constraints and subset allocation. Experimental results on two industrial text datasets demonstrate that our method achieves significant improvements in accuracy and normalized mutual information compared to the state-of-the-art method.
MENDELEEV COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Rui Chen, Yongqiang Tang, Wensheng Zhang, Wenlong Feng
Summary: In this paper, the authors propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method that optimizes multiple loss functions to improve clustering performance. The method incorporates a KL divergence based multi-view clustering loss, a semi-supervised pairwise constraint loss, and multiple autoencoders reconstruction loss, all of which are jointly optimized during network finetuning. Experimental results on eight popular image datasets demonstrate that the proposed method outperforms state-of-the-art multi-view and single-view competitors.
Article
Computer Science, Artificial Intelligence
Chenglong Dai, Jia Wu, Jessica J. M. Monaghan, Guanghui Li, Hao Peng, Stefanie I. Becker, David McAlpine
Summary: ConsEEGc is a semi-supervised graph embedding EEG clustering approach that utilizes multiple constraints to limit connections and disconnections, control intra-cluster compactness and inter-cluster scatter, and balance element ratios among different clusters, resulting in better EEG clustering results. Experiments show that ConsEEGc efficiently produces good clustering results on various types of real-world EEG datasets compared to state-of-the-art unsupervised and semi-supervised EEG/time series clustering algorithms.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Haitao Gan, Zhi Yang, Ran Zhou
Summary: Safe semi-supervised clustering (S3C) is an emerging topic in machine learning that aims to reduce the impact of wrong prior knowledge. Existing S3C methods rely on predefined formulas to estimate risk or safety degrees, which heavily affects performance. To address this issue, an adaptive safety-aware semi-supervised fuzzy c-means algorithm (AS3FCM) is proposed. AS3FCM utilizes a local consistency strategy to adaptively estimate safety degrees and constrains the outputs of labeled instances. Experimental results show that AS3FCM reasonably estimates safety degrees and achieves safe exploration of mislabeled instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Cybernetics
Kamal Berahmand, Yuefeng Li, Yue Xu
Summary: Network clustering is an unsupervised method that aims to group similar nodes together. Semi-supervised clustering detection, which utilizes side information, is a promising approach for community detection. To address the limitations of previous methods, we propose an end-to-end deep semi-supervisor community detection (DSSC) method.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)