The Experts below are selected from a list of 25968 Experts worldwide ranked by ideXlab platform
Han Liu - One of the best experts on this subject based on the ideXlab platform.
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partially related multi task Clustering
IEEE Transactions on Knowledge and Data Engineering, 2018Co-Authors: Xiaotong Zhang, Xianchao Zhang, Han Liu, Xinyue LiuAbstract:Multi-task Clustering improves the Clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task Clustering methods are based on the ideal assumption that the tasks are completely related. However, in real applications, the tasks are usually partially related. In these cases, brute-force transfer may cause negative effect which degrades the Clustering performance. In this paper, we propose two multi-task Clustering methods for partially related tasks: the self-adapted multi-task Clustering (SAMTC) method and the manifold regularized coding multi-task Clustering (MRCMTC) method, which can automatically identify and transfer related instances among the tasks, thus avoiding negative transfer. Both SAMTC and MRCMTC construct the similarity matrix for each target task by exploiting useful information from the source tasks through related instances transfer, and adopt spectral Clustering to get the Final Clustering results. But, they learn the related instances from the source tasks in different ways. Experimental results on real data sets show the superiorities of the proposed algorithms over traditional single-task Clustering methods and existing multi-task Clustering methods on both completely and partially related tasks.
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self adapted multi task Clustering
International Joint Conference on Artificial Intelligence, 2016Co-Authors: Xianchao Zhang, Xiaotong Zhang, Han LiuAbstract:Multi-task Clustering improves the Clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task Clustering methods are based on the ideal assumption that the tasks are completely related. However, in many real applications, the tasks are usually partially related, and brute-force transfer may cause negative effect which degrades the Clustering performance. In this paper, we propose a self-adapted multi-task Clustering (SAMTC) method which can automatically identify and transfer reusable instances among the tasks, thus avoiding negative transfer. SAMTC begins with an initialization by performing single-task Clustering on each task, then executes the following three steps: first, it finds the reusable instances by measuring related clusters with Jensen-Shannon divergence between each pair of tasks, and obtains a pair of possibly related subtasks; second, it estimates the relatedness between each pair of subtasks with kernel mean matching; third, it constructs the similarity matrix for each task by exploiting useful information from the other tasks through instance transfer, and adopts spectral Clustering to get the Final Clustering result. Experimental results on several real data sets show the superiority of the proposed algorithm over traditional single-task Clustering methods and existing multitask Clustering methods.
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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single point iterative weighted fuzzy c means Clustering algorithm for remote sensing image segmentation
Pattern Recognition, 2009Co-Authors: Jianchao Fan, Min Han, Jun WangAbstract:In this paper, a remote sensing image segmentation procedure that utilizes a single point iterative weighted fuzzy C-means Clustering algorithm is proposed based upon the prior information. This method can solve the fuzzy C-means algorithm's problem that the Clustering quality is greatly affected by the data distributing and the stochastic initializing the centrals of Clustering. After the probability statistics of original data, the weights of data attribute are designed to adjust original samples to the uniform distribution, and added in the process of cyclic iteration, which could be suitable for the character of fuzzy C-means algorithm so as to improve the precision. Furthermore, appropriate initial Clustering centers adjacent to the actual Final Clustering centers can be found by the proposed single point adjustment method, which could promote the convergence speed of the overall iterative process and drastically reduce the calculation time. Otherwise, the modified algorithm is updated from multidimensional data analysis to color images Clustering. Moreover, with the comparison experiments of the UCI data sets, public Berkeley segmentation dataset and the actual remote sensing data, the real validity of proposed algorithm is proved.
Zhouchen Lin - One of the best experts on this subject based on the ideXlab platform.
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convex sparse spectral Clustering single view to multi view
IEEE Transactions on Image Processing, 2016Co-Authors: Shuicheng Yan, Zhouchen LinAbstract:Spectral Clustering (SC) is one of the most widely used methods for data Clustering. It first finds a low-dimensional embedding U of data by computing the eigenvectors of the normalized Laplacian matrix, and then performs k-means on $ {\text {U}}^\top $ to get the Final Clustering result. In this paper, we observe that, in the ideal case, $ {\text {U}} {\text {U}} ^\top $ should be block diagonal and thus sparse. Therefore, we propose the sparse SC (SSC) method that extends the SC with sparse regularization on $ {\text {U}} {\text {U}} ^\top $ . To address the computational issue of the nonconvex SSC model, we propose a novel convex relaxation of SSC based on the convex hull of the fixed rank projection matrices. Then, the convex SSC model can be efficiently solved by the alternating direction method of multipliers Furthermore, we propose the pairwise SSC that extends SSC to boost the Clustering performance by using the multi-view information of data. Experimental comparisons with several baselines on real-world datasets testify to the efficacy of our proposed methods.
Xiaotong Zhang - One of the best experts on this subject based on the ideXlab platform.
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partially related multi task Clustering
IEEE Transactions on Knowledge and Data Engineering, 2018Co-Authors: Xiaotong Zhang, Xianchao Zhang, Han Liu, Xinyue LiuAbstract:Multi-task Clustering improves the Clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task Clustering methods are based on the ideal assumption that the tasks are completely related. However, in real applications, the tasks are usually partially related. In these cases, brute-force transfer may cause negative effect which degrades the Clustering performance. In this paper, we propose two multi-task Clustering methods for partially related tasks: the self-adapted multi-task Clustering (SAMTC) method and the manifold regularized coding multi-task Clustering (MRCMTC) method, which can automatically identify and transfer related instances among the tasks, thus avoiding negative transfer. Both SAMTC and MRCMTC construct the similarity matrix for each target task by exploiting useful information from the source tasks through related instances transfer, and adopt spectral Clustering to get the Final Clustering results. But, they learn the related instances from the source tasks in different ways. Experimental results on real data sets show the superiorities of the proposed algorithms over traditional single-task Clustering methods and existing multi-task Clustering methods on both completely and partially related tasks.
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self adapted multi task Clustering
International Joint Conference on Artificial Intelligence, 2016Co-Authors: Xianchao Zhang, Xiaotong Zhang, Han LiuAbstract:Multi-task Clustering improves the Clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task Clustering methods are based on the ideal assumption that the tasks are completely related. However, in many real applications, the tasks are usually partially related, and brute-force transfer may cause negative effect which degrades the Clustering performance. In this paper, we propose a self-adapted multi-task Clustering (SAMTC) method which can automatically identify and transfer reusable instances among the tasks, thus avoiding negative transfer. SAMTC begins with an initialization by performing single-task Clustering on each task, then executes the following three steps: first, it finds the reusable instances by measuring related clusters with Jensen-Shannon divergence between each pair of tasks, and obtains a pair of possibly related subtasks; second, it estimates the relatedness between each pair of subtasks with kernel mean matching; third, it constructs the similarity matrix for each task by exploiting useful information from the other tasks through instance transfer, and adopts spectral Clustering to get the Final Clustering result. Experimental results on several real data sets show the superiority of the proposed algorithm over traditional single-task Clustering methods and existing multitask Clustering methods.
Xianchao Zhang - One of the best experts on this subject based on the ideXlab platform.
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partially related multi task Clustering
IEEE Transactions on Knowledge and Data Engineering, 2018Co-Authors: Xiaotong Zhang, Xianchao Zhang, Han Liu, Xinyue LiuAbstract:Multi-task Clustering improves the Clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task Clustering methods are based on the ideal assumption that the tasks are completely related. However, in real applications, the tasks are usually partially related. In these cases, brute-force transfer may cause negative effect which degrades the Clustering performance. In this paper, we propose two multi-task Clustering methods for partially related tasks: the self-adapted multi-task Clustering (SAMTC) method and the manifold regularized coding multi-task Clustering (MRCMTC) method, which can automatically identify and transfer related instances among the tasks, thus avoiding negative transfer. Both SAMTC and MRCMTC construct the similarity matrix for each target task by exploiting useful information from the source tasks through related instances transfer, and adopt spectral Clustering to get the Final Clustering results. But, they learn the related instances from the source tasks in different ways. Experimental results on real data sets show the superiorities of the proposed algorithms over traditional single-task Clustering methods and existing multi-task Clustering methods on both completely and partially related tasks.
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self adapted multi task Clustering
International Joint Conference on Artificial Intelligence, 2016Co-Authors: Xianchao Zhang, Xiaotong Zhang, Han LiuAbstract:Multi-task Clustering improves the Clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task Clustering methods are based on the ideal assumption that the tasks are completely related. However, in many real applications, the tasks are usually partially related, and brute-force transfer may cause negative effect which degrades the Clustering performance. In this paper, we propose a self-adapted multi-task Clustering (SAMTC) method which can automatically identify and transfer reusable instances among the tasks, thus avoiding negative transfer. SAMTC begins with an initialization by performing single-task Clustering on each task, then executes the following three steps: first, it finds the reusable instances by measuring related clusters with Jensen-Shannon divergence between each pair of tasks, and obtains a pair of possibly related subtasks; second, it estimates the relatedness between each pair of subtasks with kernel mean matching; third, it constructs the similarity matrix for each task by exploiting useful information from the other tasks through instance transfer, and adopts spectral Clustering to get the Final Clustering result. Experimental results on several real data sets show the superiority of the proposed algorithm over traditional single-task Clustering methods and existing multitask Clustering methods.