检索结果-内蒙古大学图书馆

IEEE Radar Conference (RadarConf)

作者： Tastan, Aylin Muma, Michael Zoubir, Abdelhak M. Tech Univ Darmstadt Signal Proc Grp D-64283 Darmstadt Germany

ISBN: (纸本)9781728189420

Classification of gait abnormalities plays a key role in medical diagnosis, sports, physiotherapy and rehabilitation. We demonstrate the effectiveness of a new graph construction-based outlier detection method and and the applicability of a new parameter-free clustering approach on radar-based human gait signatures. Micro-Doppler radar-based human gait signatures of ten test subjects for five different gait types consisting of normal, simulated abnormal and assisted walks are clustered using five different clustering algorithms. The proposed algorithm outperforms existing methods both in cluster enumeration and partition and achieves an overall correct clustering rate of 92.8%. The developed method is promising for performing medical diagnosis in a robust unsupervised fashion.

关键词： graph-based clustering parameter-free clustering outlier detection sparse gait recognition community detection

来源：评论

学校读者我要写书评

暂无评论

ROBUST RANK CONSTRAINED SPARSE LEARNING: A graph-based METHOD FOR clustering

ROBUST RANK CONSTRAINED SPARSE LEARNING: A GRAPH-BASED METHO...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Liu, Ran Chen, Mulin Wang, Qi Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

graph-based clustering is an advanced clustering techniuqe, which partitions the data according to an affinity graph. However, the graph quality affects the clustering results to a large extent, and it is difficult to construct a graph with high quality, especially for data with noises and outliers. To solve this problem, a robust rank constrained sparse learning method is proposed in this paper. The L-2,(1)-norm objective function of sparse representation is introduced to learn the optimal graph with robustness. To preserve the data structure, the graph is searched within the neighborhood of the initial graph. By incorporating a rank constraint, the learned graph can be directly used as the cluster indicator and the final results is obtained without additional post-processing. Plenty of experiments on real-world data sets have proved the superiority and the robustness of the proposed approach.

关键词： Unsupervised Learning clustering graph-based clustering Sparse Representation ALM

来源：评论

学校读者我要写书评

暂无评论

graph-based Sequence clustering through Multiobjective Evolutionary Algorithms for Web Recommender Systems 07

Graph-based Sequence Clustering through Multiobjective Evolu...

引用

Annual Conference of Genetic and Evolutionary Computation Conference

作者： Demir, Gul Nildem Uyar, A. Sima Oguducu, Sule Istanbul Tech Univ Dept Comp Engn TR-34469 Istanbul Turkey

ISBN: (纸本)9781595936974

In web recommender systems, clustering is done offline to extract usage patterns and a successful recommendation highly depends oil the quality, Of this clustering solution. lit these types of applications, data to be clustered is in the form of user sessions which area sequences of web pages visited by the user. Sequence clustering is one of the important tools to work with this type of data. One way to represent sequence data is through weighted, undirected graphs where each sequence, is a vertex and the pairwise similarities between the user session,, are the edges. Through this representation, the problem becomes equivalent to graph partitioning which is NP-complete and is best, approached using multiple objectives. Hence it is suitable to use multiobjective evolutionary algorithms (MOEA) to solve it. The main focus of this paper is to determine an effective MOEA to cluster sequence data.. Several existing approaches in literature are compared oil sample data sets and the most suitable approach is determined.

关键词： graph-based clustering sequence clustering multiobjective evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Fast self-supervised discrete graph clustering with ensemble local cluster constraints

引用

NEURAL NETWORKS 2025年 188卷 107421页

作者： Yang, Xiaojun Li, Bin Zhao, Weihao Xu, Sha Xue, Jingjing Nie, Feiping Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Guangdong Peoples R China Key Lab Marine Convergent Sensing & Tridomain Unma Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Technol Sch Integrated Circuits Guangzhou 510006 Guangdong Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Shanxi Peoples R China Northwestern Polytech Univ Xian 710072 Shanxi Peoples R China

Spectral clustering (SC) is a graph-based clustering algorithm that has been widely used in the field of data mining and image processing. However, most graph-based clustering methods ignore the utilization of additional prior information. This information can help clustering models further reduce the difference between their clustering results and ground-truth, but is difficult to obtain in unsupervised settings. Moreover, traditional graph-based clustering algorithms require additional hyperparameters and full graph construction to obtain good performance, increasing the tuning pressure and time cost. To address these issues, a simple fast self-supervised discrete graph clustering (FSDGC) is proposed. Specifically, the proposed method has the following features: (1) a novel self-supervised information, based on ensemble local cluster constraints, is used to constrain the sample indicator matrix;(2) the anchor graph technique is introduced for mining the structure between samples and anchors to handle large scale datasets. Meanwhile, a fast coordinate ascent (CA) optimization method, based on self-supervised constraints, is proposed to obtain discrete indicator matrices. Experimental clustering results demonstrate that FSDGC has efficient clustering performance.

关键词： graph-based clustering Discrete clustering Self-supervised information Anchor graph Coordinate ascent (CA)

来源：评论

学校读者我要写书评

暂无评论

GMC: graph-based Multi-View clustering

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第6期32卷 1116-1129页

作者： Wang, Hao Yang, Yan Liu, Bing Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general graph-based Multi-view clustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified graph matrix. The unified graph matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its learning method, which can help the learning of each view graph matrix and the learning of the unified graph matrix in a mutual reinforcement manner. A novel multi-view fusion technique can automatically weight each data graph matrix to derive the unified graph matrix. A rank constraint without introducing a tuning parameter is also imposed on the graph Laplacian matrix of the unified matrix, which helps partition the data points naturally into the required number of clusters. An alternating iterative optimization algorithm is presented to optimize the objective function. Experimental results using both toy data and real-world data demonstrate that the proposed method outperforms state-of-the-art baselines markedly.

关键词： Matrix converters clustering algorithms clustering methods Laplace equations Computational modeling Computational complexity Kernel Multi-view clustering graph-based clustering data fusion Laplacian matrix rank constraint

来源：评论

学校读者我要写书评

暂无评论

An improved feature selection algorithm based on graph clustering and ant colony optimization

引用

KNOWLEDGE-based SYSTEMS 2018年 159卷 270-285页

作者： Ghimatgar, Hojat Kazemi, Kamran Helfroush, Mohamamd Sadegh Aarabi, Ardalan Shiraz Univ Technol Dept Elect & Elect Engn POB 71555-313 Shiraz Iran CHU AMIENS Univ Res Ctr CURS LFNP EA4559 SITE SUDAve Laennec F-80420 Salouel France Univ Picardie Jules Verne UPJV Fac Med F-80036 Amiens France

Dimensionality reduction is an important preprocessing step to improve the performance of machine learning algorithms. Feature selection methods can efficiently speed up the learning process and improve the overall classification accuracy by reducing the computational complexity. Among the feature selection methods, multivariate methods are more effective in removing irrelevant and redundant features. An efficient multivariate feature selection method, optimization method, called 'graph clustering based ant colony optimization (GCACO)' has been recently introduced and shown to outperform other well-known feature selection methods. In the GCACO, features are divided into communities (clusters) in the entire feature space represented as a graph by an efficient community detection algorithm. An ACO-based search strategy is then used to select an optimal feature subset from the initial set of features. In this paper, a modified GCACO algorithm called MGCACO is presented to significantly improve the performance of the GCACO. Performance of the MGCACO algorithm was assessed by testing it on several standard benchmark datasets and sleep EEG data. The performance of the MGCACO was compared to those obtained using the original GCACO and other well-known filtering methods available in the literature. The MGCACO achieved superior performance over the GCACO and other univariate and multivariate algorithms with up to 10%. The MGCACO also exhibited higher efficiency in reducing the number of features all by keeping the classification accuracy maximum.

关键词： Feature selection graph-based clustering Ant colony optimization Mutual information Multiple discriminant analysis

来源：评论

学校读者我要写书评

暂无评论

A robust clustering method with noise identification based on directed K-nearest neighbor graph

引用

NEUROCOMPUTING 2022年 508卷 19-35页

作者： Li, Lin Chen, Xiang Song, Chengyun Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Obtaining the optimal cluster number and generating reliable clustering results in nonlinear manifolds are necessary but challenging tasks. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns, while graph-based clustering has shown impressive performance in identifying clusters in such data patterns. In this paper, we propose a robust clustering method with noise cutting based on directed k-nearest neighbor graph (CDKNN) to identify the desired cluster number automatically and produce reliable clustering results simultaneously on nonlinear, non-overlapping but locally tight-connected data patterns. This method draws support from the k-nearest neighbor graph to represent the complex nonlinear datasets and applies parameter adaptive process to make the proposed clustering method better adapt to specific data patterns. The proposed method is robust to the noises of arbitrary shape datasets because it uses a directed K-nearest neighbor to cut out sparse nodes. We use simulation and UCI real-world datasets to prove the validity of the innovatory method by comparing it to k-means, DBSCAN, OPTICS, AP, SC, and CutPC algorithms in terms of clustering ACC, ARI, NMI, and FMI. The experimental results confirm that the proposed method outperforms the alternative nonlinear clustering methods. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Directed k-nearest neighbor graph graph-based clustering Unsupervised learning Nonlinear dataset Noise cutting

来源：评论

学校读者我要写书评

暂无评论

Robust graph-based multi-view clustering in latent embedding space

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第2期13卷 497-508页

作者： Mei, Yanying Ren, Zhenwen Wu, Bin Shao, Yanhua Yang, Tao Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Southwest Univ Sci & Technol Sch Natl Def Sci & Technol Mianyang 621010 Sichuan Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Peoples R China

Multi-view graph-based clustering (MGC) aims to cluster multi-view data via a graph learning scheme, and has aroused widespread research interests in behavior detection, face recognition, and information retrieval in recent years. However, most of the existing MGC methods usually learn the affinity graph in the original space, such that they are inevitably hindered by the curse of dimensionality and corrupted features. Moreover, they usually learn the affinity between paired-samples by using Euclidean-distance metric;nevertheless, such a metric is sensitive to noise and outliers. In this paper, we propose a novel MGC method, namely latent embedding space learning (LESL), which aims to learn a latent embedding space and a robust affinity graph simultaneously. Specifically, a latent embedding representation is firstly learned, which can reduce the corruption and redundancy of the original views, and can effectively utilize the complementary information of multiple views. Afterwards, a robust estimator is used to automatically cut the connections among inter-cluster in the affinity graph. Finally, alternating direction minimization on the augmented Lagrangian multiplier (ALM-ADM) is adopted to optimize the unified objective function. Experimental results show that LESL outperforms state-of-the-art methods obviously.

关键词： Multi-view data graph-based clustering Latent embedding representation Robust affinity graph

来源：评论

学校读者我要写书评

暂无评论

A graph-theoretical clustering method based on two rounds of minimum spanning trees

引用

PATTERN RECOGNITION 2010年第3期43卷 752-766页

作者： Zhong, Caiming Miao, Duoqian Wang, Ruizhi Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China Minist Educ China Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China Ningbo Univ Coll Sci & Technol Ningbo 315211 Zhejiang Peoples R China

Many clustering approaches have been proposed in the literature, but most of them are vulnerable to the different cluster sizes, shapes and densities. In this paper, we present a graph-theoretical clustering method which is robust to the difference. based on the graph composed of two rounds of minimum spanning trees (MST), the proposed method (2-MSTClus) classifies cluster problems into two groups, i.e. separated cluster problems and touching cluster problems, and identifies the two groups of cluster problems automatically. it contains two clustering algorithms which deal with separated clusters and touching clusters in two phases, respectively. In the first phase, two round minimum spanning trees are employed to construct a graph and detect separated clusters which cover distance separated and density separated clusters. In the second phase, touching clusters, which are subgroups produced in the first phase, can be partitioned by comparing cuts, respectively, on the two round minimum spanning trees. The proposed method is robust to the varied cluster sizes, shapes and densities, and can discover the number of clusters. Experimental results on synthetic and real datasets demonstrate the performance of the proposed method. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： graph-based clustering Well-separated cluster Touching cluster Two rounds of MST

来源：评论

学校读者我要写书评

暂无评论

clustering high dimensional data: A graph-based relaxed optimization approach

引用

INFORMATION SCIENCES 2008年第23期178卷 4501-4511页

作者： Lee, Chi-Hoon Zaiane, Osmar R. Park, Ho-Hyun Huang, Jiayuan Greiner, Russell Univ Alberta Dept Comp Sci Edmonton AB T6G 2M7 Canada Chung Ang Univ Sch Elect & Elect Engn Seoul South Korea Univ Waterloo Sch Comp Sci Waterloo ON N2L 3G1 Canada

There is no doubt that clustering is one of the most studied data mining tasks. Nevertheless, it remains a challenging problem to solve despite the many proposed clustering approaches. graph-based approaches solve the clustering task as a global optimization problem, while many other works are based on local methods. in this paper, we propose a novel graph-based algorithm "GBR" that relaxes some well-defined method even as improving the accuracy whilst keeping it simple. The primary motivation of our relaxation of the objective is to allow the reformulated objective to find well distributed cluster indicators for complicated data instances. This relaxation results in an analytical solution that avoids the approximated iterative methods that have been adopted in many other graph-based approaches. The experiments on synthetic and real data sets show that our relaxation accomplishes excellent clustering results. Our key contributions are: (1) we provide an analytical solution to solve the global clustering task as opposed to approximated iterative approaches;(2) a very simple implementation using existing optimization packages: (3) an algorithm with relatively less computation time over the number of data instances to cluster than other well defined methods in the literature. (C) 2008 Elsevier Inc. All rights reserved.

关键词： clustering High dimensional data Relaxed optimization graph-based clustering

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：