Convex Discriminative Multitask Clustering

By Xiao-Lei Zhang. 2013

58 citations recorded by the source corpus.

Multitask clustering tries to improve the clustering performance of multiple tasks simultaneously by taking their relationship into account. Most existing multitask clustering algorithms fall into the type of generative clustering, and none are formulated as convex optimization problems. In this paper, we propose two convex Discriminative Multitask Clustering (DMTC) objectives to address the problems. The first one aims to learn a shared feature representation, which can be seen as a technical combination of the convex multitask feature learning and the convex Multiclass Maximum Margin Clustering (M3C). The second one aims to learn the task relationship, which can be seen as a combination of the convex multitask relationship learning and M3C. The objectives of the two algorithms are solved in a uniform procedure by the efficient cutting-plane algorithm and further unified in the Bayesian framework. Experimental results on a toy problem and two benchmark data sets demonstrate the effectiveness of the proposed algorithms.

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. Mohan Kankanhalli
7. Jiebo Luo
8. Anan Liu

In Computer Vision and Pattern Recognition

1. Qiang Yang
2. Yu Zhang
3. 刘晗
4. Xiaotong Zhang
5. Xianchao Zhang
6. Mohan Kankanhalli
7. Anan Liu
8. Yuting Su

Citations in the corpus

• An Overview of Multi-task Learning (2018, neutral): “In [62], two multi-task-clustering methods are proposed.”
• A Survey on Multi-Task Learning (2017, methodology): “In [129], two proposed multi-task clustering methods are extensions of the MTFL and MTRL methods by treating labels as cluster indicators to be learned.”
• Smart Multitask Bregman Clustering and Multitask Kernel Clustering (2015, methodology): “For DMTRC-L and DMTRC-R, we apply the grid searching method to identify the parameters [Zhang 2015].”
• Multi-Task Clustering with Model Relation Learning (2018, methodology): “…and Kernel clustering (S-MBC and S-MKC) methods [Zhang et al. , 2015], the convex discriminative multi-task feature clustering (DMTFC) method [Zhang, 2015], the convex discriminative multi-task relationship clustering (DMTRC) method [Zhang, 2015], the multi-task spectral clustering (MTSC)…”
• Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition (2017, methodology): “We notice that several methods [13], [14] have been developed to leverage task grouping information for model learning in the machine learning society.”
• Multi-Task Multi-View Clustering (2016, methodology): “We compare the BMTMVC algorithm and SMTMVC algorithm with (1) typical single-task single-view clustering methods: k-means, normalized spectral clustering algorithm (NSC) [67], kernel k-means (kk-means), bipartite graph co-clustering algorithm (BiCo) [48], the semi-nonnegative matrix tri-factorizatio”
• Self-Adapted Multi-Task Clustering (2016, methodology): “We also compare SAMTC with the multi-task clustering methods which work for completely related tasks: the shared subspace learning multi-task clustering (LSSMTC) method [Gu and Zhou, 2009] and the convex discriminative multi-task feature clustering (MTFC) method [Zhang, 2015].”