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Discriminative Low-Rank Representation for HSI clustering

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Li, Zhixin Han, Bo Jia, Yuheng Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Minist Educ Sch Comp Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Minist Educ Key Lab New Generat Artificial Intelligence Techno Nanjing 210096 Peoples R China

We exploit the hyperspectral image (HSI) clustering problem, which partitions an input HSI into several groups without relying on any supervision information. The previous methods mainly focus on developing an HSI clustering technique, which neglects learning a discriminative representation. To this end, this letter proposes a novel discriminative low-rank representation method to exploit the spatial and spectral information of HSIs. The proposed method is formulated as a concave-convex optimization problem and solved by alternating direction method of multipliers. By applying a simple clustering technique (such as K-means) on the obtained discriminative low-rank representation, our method can produce better clustering performance than the state-of-the-art HSI clustering methods. Experiments on four benchmark datasets confirm the superior clustering performance of the proposed method. The code of this letter is available at: https://***/LZX-001/Discriminative_Low_Rank_Representation_for _HSI_clustering.

关键词： clustering methods Hyperspectral imaging Optimization Geoscience and remote sensing Convex functions clustering algorithms Indexes clustering discriminative representation hyperspectral image (HSI)

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Globality Meets Locality: An Anchor Graph Collaborative Learning Framework for Fast Multiview Subspace clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第6期PP卷 PP页

作者： Guo, Jipeng Sun, Yanfeng Ma, Xin Gao, Junbin Hu, Yongli Wang, Youqing Yin, Baocai Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Beijing Univ Technol Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Univ Sydney Business Sch Discipline Business Analyt Camperdown NSW 2006 Australia

Multiview subspace clustering (MSC) maximizes the utilization of complementary description information provided by multiview data and achieves impressive clustering performance. However, most of them are inefficient or even invalid among large-scale scenarios due to expensive computational complexity. Recently, anchor strategy has been developed to address this, which selects a few representative samples as anchor points for representation learning and anchor graph construction. However, most of them only explore single cross-view correlation, i.e., cross-view consistency from the global aspect or cross-view complementarity from the local aspect, which provides insufficient semantic correlation understanding and exploration for complex multiview data. To effectively address this issue, this study proposes a fast multiview subspace clustering (FMSC) with local-global anchor representation collaborative learning. FMSC integrates the discriminative anchor points learning and anchor graph construction with optimal structure into a joint framework. Furthermore, local (view-specific) and global (view-shared) anchor representations are learned collaboratively under two interaction strategies at different levels, providing beneficial guidance from global learning to local learning. Thus, the proposed FMSC can maximize the exploration of the complementarity-consistency among multiview data and capture a more comprehensive semantic correlation. More importantly, an effective algorithm with linear complexity is designed to solve the corresponding optimization problem of FMSC, making it more practical in large-scale clustering tasks. Extensive experimental results confirm the superiority of the proposed FMSC in both clustering performance and computational efficiency.

关键词： Correlation Representation learning Optimization Computational complexity Bipartite graph Semantics Federated learning Computational efficiency clustering algorithms Tensors Collaborative interaction fast multiview subspace clustering (FMSC) global-local anchor representation optimal bipartite graph learning

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clustering Study of Commercial and Industrial Power Load Profiles Based on Knndp-Pfcm with Weighted Fusion Similarity Measure

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第4期52卷 1116-1136页

作者： Xie, Tao Zhang, Chaobin Tan, Zhiqiang Xu, Jie Chongqing University of Posts and Telecommunications Chongqing400065 China Huaneng Chongqing Energy Sales Co. Ltd Chongqing401121 China State Grid Chongqing Electric Power Dispatch and Control Center Chongqing400014 China

The volume of power load data has increased significantly with the proliferation of smart grids. clustering of power load profiles plays a crucial role in identifying distinct consumption patterns and load characteristics. However, traditional clustering algorithms encounter challenges, such as difficulties in selecting suitable initial cluster centers and the inability of Euclidean distance to adequately capture the complexity of load profile data. These issues can result in suboptimal clustering performance. Therefore, this study proposes a novel clustering algorithm to address these challenges. The proposed algorithm uses an improved density peak clustering method to determine the initial cluster centers, incorporates an adaptive weighting approach to account for the dynamic nature of the load profiles, and introduces a novel similarity measure that combines Euclidean and Pearson distances to more effectively capture the relationships between the load profiles. The performance of the proposed algorithm is evaluated using two datasets of smart meter data from Southwest China. The computational results demonstrate that the proposed algorithm outperforms other algorithms in terms of the sum of squared errors within clusters (SSE), the Davies-Bouldin Index (DBI), the silhouette coefficient (SC), and the Calinski-Harabasz Index (CHI), as well as in algorithmic stability and iterative efficiency. The proposed algorithm also exhibits strong robustness when applied to datasets with different levels of noise perturbation. This study also explores the impact of different algorithm parameters and initial clustering centers on the clustering results through a comprehensive analysis. Overall, the results indicate that the proposed method exhibits high robustness and strong applicability in practical scenarios. © (2025), (International Association of Engineers). All rights reserved.

关键词： clustering algorithms

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A study of hierarchical clustering algorithms

A study of hierarchical clustering algorithms

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sakshi Patel Shivani Sihmar Aman Jatain Department of Computer Science ITM University Gurgaon INDIA

clustering algorithm plays a vital role in organizing large amount of information into small number of clusters which provides some meaningful information. clustering is a process of categorizing set of objects into groups called clusters. Hierarchical clustering is a method of cluster analysis which is used to build hierarchy of clusters. This paper focuses on hierarchical agglomerative clustering. In this paper, we also explain some agglomerative algorithms and their comparison.

关键词： clustering algorithms Algorithm design and analysis Heuristic algorithms Partitioning algorithms Couplings Complexity theory Rocks

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Structured Graph-Based Ensemble clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第6期37卷 3728-3738页

作者： Zheng, Xuan Lu, Yihang Wang, Rong Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Cybersecur Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Ensemble clustering can utilize the complementary information among multiple base clusterings, and obtain a clustering model with better performance and more robustness. Despite its great success, there are still two problems in the current ensemble clustering methods. First, most ensemble clustering methods often treat all base clusterings equally. Second, the final ensemble clustering result often relies on k-means or other discretization procedures to uncover the clustering indicators, thus obtaining unsatisfactory results. To address these issues, we proposed a novel ensemble clustering method based on structured graph learning, which can directly extract clustering indicators from the obtained similarity matrix. Moreover, our methods take sufficient consideration of correlation among the base clusterings and can effectively reduce the redundancy among them. Extensive experiments on artificial and real-world datasets demonstrate the efficiency and effectiveness of our methods.

关键词： clustering methods clustering algorithms Partitioning algorithms Trajectory Training Robustness Redundancy Optics Machine learning algorithms Laplace equations clustering ensemble learning structured graph learning

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A Novel Snow Depth Retrieving Approach Using Time-Series clustering in GPS-IR Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Wang, Tianyu Zhang, Rui Liu, Anmengyun Yang, Yunjie Lv, Jichao Jiang, Yao Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 610031 Peoples R China

Affected by the surrounding environment of the station and global positioning system (GPS) satellite signal receiving conditions, there are significant fluctuations in the snow depth retrieval results of the GPS interferometric reflection (GPS-IR) based on GPS data acquired in different trajectories or frequency bands, resulting in the loss of snow depth retrieval accuracy and reliability. Therefore, this contribution proposes a novel snow depth retrieving approach using time-series clustering (TSC) in GPS-IR data. For improving the adaptability of the algorithm to different application scenarios, dynamic time warping (DTW) is introduced to perform K-medoids clustering on the snow depth retrieval sequence of all available satellite trajectories, and the daily time-series snow depth retrieval results are obtained based on the clustering center evaluation index. For validation purposes, multifrequency GPS data were selected for snow depth retrieval. The results indicate that, compared with traditional approaches, the proposed TSC method demonstrated higher robustness and accuracy, with correlation coefficients reaching above 0.975 in all frequency bands. The root mean square difference (RMSD) for snow depth retrieval at P351 and AB39 stations, using GPS L1 frequency data, was 8.772 and 6.095 cm, respectively. The TSC algorithm proposed in this letter classifies the snow depth retrieval sequences of different trajectories, achieving an adaptive acquisition of the optimal snow depth retrieval sequence, thus greatly reducing the workload of manual intervention and preliminary data quality assessment in GPS-IR snow depth retrieval. The proposed GPS-IR snow depth retrieval algorithm may offer a new technical approach for future related research.

关键词： Snow Satellites Signal to noise ratio clustering algorithms Time series analysis Global Positioning System Reflection Dynamic time warping (DTW) global positioning system interferometric reflectometry (GPS-IR) K-medoids clustering snow depth retrievals time-series clustering (TSC)

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Graceful Register clustering and Rebanking for Power and Timing Balancing

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第3期44卷 1070-1083页

作者： Hsieh, Yi-Ting Lin, Tung-Wei Chang, Ya-Chu Wu, Hao-Yu Jiang, Iris Hui-Ru Synopsys Inc Design Technol Grp Taipei 110 Taiwan Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Innovus Cadence Design Syst Hsinchu Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei 106319 Taiwan

As dynamic power has become the bottleneck to achieving low power, clock power reduction is crucial in modern IC design. Register clustering can effectively save clock power because of significantly reducing the number of clock sinks and register pin capacitance, clock routed wirelength, and the number of clock buffers. In this article, we propose effective mean shift to naturally form clusters according to register distribution without placement disruption. Effective mean shift fulfills the requirements to be a good register clustering algorithm because it needs no prespecified number of clusters, is insensitive to initializations, is robust to outliers, is tolerant of various register distributions, is efficient and scalable, and balances clock power reduction against timing degradation. Furthermore, we devise a rebanking mechanism to legalize the clustering result according to the adopted multibit register library. Experimental results show that our approach achieves superior power and timing balancing.

关键词： Registers Clocks Timing Kernel Bandwidth Degradation clustering algorithms Clock power clustering mean shift rebanking register clustering timing

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Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on clustering Results

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY

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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2025年第1期6卷 438-448页

作者： Elnour, Ammar Yang, Wencheng Li, Yan Univ Southern Queensland Sch Math Phys & Comp Toowoomba Qld 4350 Australia

clustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzing result uncertainty. This issue applies to most outcomes obtained from other knowledge discovery techniques, such as machine learning and statistical learning. Traditional statistical methods assume data follows standard distributions, whereas resampling and bootstrapping methods offer more accurate and reliable alternatives. This article introduces a method that employs bootstrap likelihood estimation to infer the uncertainty of generated clustering structures. We first calculated the clustering error in the original dataset and then utilized the proposed method to estimate its nonparametric bootstrapped likelihood. By comparing these two values, we can establish a nonparametric significance testing framework that directly determines the validity of the result. To evaluate the effectiveness of our method, we conducted experiments using synthetic and real datasets. The results demonstrate that our method can successfully validate clustering results.

关键词： Uncertainty clustering algorithms Machine learning Estimation Statistical learning Surveys Standards Noise measurement Testing Partitioning algorithms clustering validity testing randomness bootstrap likelihood significance testing statistical machine learning

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Heat Kernel Diffusion for Enhanced Late Fusion Multi-View clustering

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2310-2314页

作者： Yang, Gui Zou, Jiale Chen, Yan Du, Liang Zhou, Peng Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan 030024 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Recent advancements in Multi-view clustering (MVC) have highlighted the benefits of late fusion techniques. However, existing late fusion-based MVC (LFMVC) approaches often struggle with intrinsic noise and redundancy within base clustering embeddings generated by traditional method, and fail to capture higher-order correlations among samples and across views. We propose a novel method that incorporates an optimal consensus heat kernel-induced graph filter to address these issues. Our approach leverages diffusion processes to construct filters that capture high-order information, achieving neighborhood smoothing while maintaining local consistency within each view. By integrating a consensus graph filter with downstream clustering tasks within a unified learning framework, our method enhances performance through mutual reinforcement. In particular, our method does not require additional hyperparameter tuning, simplifying the optimization process. Experimental results on various benchmark datasets demonstrate the superior effectiveness of our approach, outperforming state-of-the-art methods in terms of clustering performance.

关键词： Kernel Information filters Heating systems Optimization Noise Task analysis clustering algorithms Late fusion multi-view clustering smoothed representation

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Sparseness and Correntropy-Based Block Diagonal Representation for Robust Subspace clustering

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1154-1158页

作者： Xu, Yesong Hu, Ping Dai, Jiashu Yan, Nan Wang, Jun Anhui Polytech Univ Sch Comp & Informat Wuhu 241000 Peoples R China

Block diagonal representation, which aims to compel the desired representation coefficient to have a block diagonal structure directly, has extensive applications in the domains of computer vision and machine learning. However, single residual modeling in existing works is not robust enough when handling complex noise (i.e., sparse noise and impulsive noise) in reality. To overcome this challenge, a novel Sparseness and Correntropy-based Block Diagonal Representation (SC-BDR) model is proposed, which is able to pursue ideal block diagonal representation and effectively deal with various types of noise. Furthermore, the corresponding optimization algorithm is designed for the proposed problem, and we also conduct extensive experiments to demonstrate the robustness and effectiveness of the SC-BDR model on real-world data.

关键词： Noise Sparse matrices Robustness clustering algorithms Vectors Optimization Minimization Block diagonal representation low-rank representation subspace clustering noise

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