In recent years,multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical applications.Despite the significant ...In recent years,multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical applications.Despite the significant advances in multi-view clustering,two challenges still need to be addressed,i.e.,how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for clustering.In this study,we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering(TW-Co-MFC)approach to address the aforementioned issues.In TW-Co-MFC,a two-level weighting strategy is devised to measure the importance of views and features,and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering consistency.Then an iterative optimization objective function based on the maximum entropy principle is designed for multi-view clustering.Experiments on real-world datasets show the effectiveness of the proposed approach.展开更多
针对杂波环境下的多目标跟踪数据关联存在跟踪精度低、实时性差的问题,提出了一种基于最大熵模糊聚类的联合概率数据关联算法(joint probabilistic data association algorithm based on maximum entropy fuzzy clustering,MEFC-JPDA)...针对杂波环境下的多目标跟踪数据关联存在跟踪精度低、实时性差的问题,提出了一种基于最大熵模糊聚类的联合概率数据关联算法(joint probabilistic data association algorithm based on maximum entropy fuzzy clustering,MEFC-JPDA)。首先,采用最大熵模糊聚类求得的隶属度初步表征目标与有效量测之间的关联概率。其次,采用基于目标距离的量测修正因子对关联概率进行调整,并建立关联概率矩阵。最后,结合卡尔曼滤波算法,对目标的状态进行加权更新。仿真结果表明,所提算法在杂波环境下的跟踪性能相比现有的两种关联算法有较大提升,是一种有效的多目标跟踪数据关联算法。展开更多
基金supported by the National Natural Science Foundation of China(Nos.61603313,61772435,61976182,and 61876157)。
文摘In recent years,multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical applications.Despite the significant advances in multi-view clustering,two challenges still need to be addressed,i.e.,how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for clustering.In this study,we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering(TW-Co-MFC)approach to address the aforementioned issues.In TW-Co-MFC,a two-level weighting strategy is devised to measure the importance of views and features,and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering consistency.Then an iterative optimization objective function based on the maximum entropy principle is designed for multi-view clustering.Experiments on real-world datasets show the effectiveness of the proposed approach.
文摘针对杂波环境下的多目标跟踪数据关联存在跟踪精度低、实时性差的问题,提出了一种基于最大熵模糊聚类的联合概率数据关联算法(joint probabilistic data association algorithm based on maximum entropy fuzzy clustering,MEFC-JPDA)。首先,采用最大熵模糊聚类求得的隶属度初步表征目标与有效量测之间的关联概率。其次,采用基于目标距离的量测修正因子对关联概率进行调整,并建立关联概率矩阵。最后,结合卡尔曼滤波算法,对目标的状态进行加权更新。仿真结果表明,所提算法在杂波环境下的跟踪性能相比现有的两种关联算法有较大提升,是一种有效的多目标跟踪数据关联算法。
