Smart Multi-Task Bregman Clustering and Multi-Task Kernel Clustering

By Xianchao Zhang, Xiaotong Zhang. 2013

15 citations recorded by the source corpus.

Multitask Bregman Clustering (MBC) alternatively updates clusters and learns relationship between clusters of different tasks, and the two phases boost each other. However, the boosting does not always have positive effect, it may also cause negative effect. Another issue of MBC is that it cannot deal with nonlinear separable data. In this paper, we show that MBC's process of using cluster relationship to boost the updating clusters phase may cause negative effect, i.e., cluster centroid may be skewed under some conditions. We propose a smart multi-task Bregman clustering (S-MBC) algorithm which identifies negative effect of the boosting and avoids the negative effect if it occurs. We then extend the framework of S-MBC to a smart multi-task kernel clustering (S-MKC) framework to deal with nonlinear separable data. We also propose a specific implementation of the framework which could be applied to any Mercer kernel. Experimental results confirm our analysis, and demonstrate the superiority of our proposed methods.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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. Xinyue Liu
7. Mingsheng Long
8. Jianmin Wang

In Computer Vision and Pattern Recognition

1. Qiang Yang
2. Yu Zhang
3. 刘晗
4. Xiaotong Zhang
5. Xianchao Zhang
6. Xinyue Liu
7. Gabriella Csurka
8. Dacheng Tao

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”
• Multi-Task Multi-View Clustering (2016, background): “The graph-based framework in [1] takes full advantages of both the feature heterogeneity and task heterogeneity.”
• Self-Adapted Multi-Task Clustering (2016, methodology): “The method in [Zhang and Zhou, 2012] learns a shared subspace in which the distributions of the related tasks are close to each other.”
• Smart Multitask Bregman Clustering and Multitask Kernel Clustering (2015, methodology): “Unlike the earlier methods, in the pre-liminary version of this article [Zhang and Zhang 2013], we proposed a step-by-step monitoring scheme to avoid negative transfer of the MBC algorithm.”