Multitask Bregman clustering

By Jianwen Zhang, Changshui Zhang. 2010

56 citations recorded by the source corpus.

Traditional clustering methods deal with a single clustering task on a single data set. However, in some newly emerging applications, multiple similar clustering tasks are involved simultaneously. In this case, we not only desire a partition for each task, but also want to discover the relationship among clusters of different tasks. It's also expected that the learnt relationship among tasks can improve performance of each single task. In this paper, we propose a general framework for this problem and further suggest a specific approach. In our approach, we alternatively update clusters and learn relationship between clusters of different tasks, and the two phases boost each other. Our approach is based on the general Bregman divergence, hence it's suitable for a large family of assumptions on data distributions and divergences. Empirical results on several benchmark data sets validate the approach.

Subfields: Artificial Intelligence, Media Technology, Modeling and Simulation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Yu Zhang
3. 刘晗
4. Xiaotong Zhang
5. Xianchao Zhang
6. Xiao-Lei Zhang
7. Jiebo Luo
8. Chaoyue Liu

In Media Technology

1. Xianchao Zhang
2. Xiaotong Zhang
3. 刘晗
4. Mikhail Belkin
5. Qiang Yang
6. Xiao-Lei Zhang
7. Jiebo Luo
8. Yu Zhang

In Modeling and Simulation

1. Qiang Yang
2. Xianchao Zhang
3. Xiao-Lei Zhang
4. Jiebo Luo
5. Yu Zhang
6. Xiaotong Zhang
7. 刘晗

Citations in the corpus

• A Survey on Multi-Task Learning (2017, methodology): “In [125], a multi-task Bregman clustering method is proposed based on single-task Bregman clustering by using the earth mover distance to minimize distances between any pair of tasks in terms of cluster centers and then in [126], [127], an improved version of [125] and its kernel extension are propo”
• Convex Discriminative Multitask Clustering (2013, methodology): “Only very recently, it received more and more attention [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47].”
• Multi-Task Clustering with Model Relation Learning (2018, methodology): “MBC [ Zhang and Zhang, 2010 ] and its improved methods (S-MBC and S-MKC) [ Zhang et al. , 2015 ] alternatively update the clusters and learn the relationship between clusters of different tasks.”
• Clustering with Bregman Divergences: an Asymptotic Analysis (2016, background): “That work has lead to a new line of research on clustering including results on multitask Bregman clustering[24], agglomerative”
• Smart Multitask Bregman Clustering and Multitask Kernel Clustering (2015, methodology): “Given W and H , clustering centroids M ( t ) is determined by min We can find that each centroid m ( t ) z in M ( t ) is independently determined by min With the following two properties of Bregman divergences mentioned in Zhang and Zhang [2010], the problem can be easily solved.”
• Smart Multi-Task Bregman Clustering and Multi-Task Kernel Clustering (2013, methodology): “(Zhang and Zhang 2010) proposed a multitask Bregman clustering algorithm, which alternatively updates the single-task Bregman clustering and learns the relationship between clusters of different tasks, and the two phases boost each other.”
• Self-Adapted Multi-Task Clustering (2016, methodology): “Two kinds of multi-task clustering methods for partially related tasks based on some assumptions have been proposed: 1) MBC [Zhang and Zhang, 2010] and its improved versions S-MBC and S-MKC [Zhang and Zhang, 2013] update the clusters by learning the relationship between clusters of different tasks.”
• Multi-Task Multi-View Clustering for Non-Negative Data (2015, methodology): “Unlike the above methods which focus on cross-domain multi-task clustering, some multi-task clustering methods are proposed to deal with the case of the related tasks from a same distribution or similar distributions [Zhang and Zhang, 2010; 2013].”
• A Survey on Multi-View Clustering (2017, unknown): “Multi-task clustering aims to improve the performance of unsupervised clustering tasks, such as [123], [124], [125], [126], [127].”
• Multi-Task Multi-View Clustering (2016, background): “However, we can get better performance if both the multi-task and multi-view information could be utilized.”