Multi-Task Clustering with Model Relation Learning

By Xiaotong Zhang, Xianchao Zhang, 刘晗, Jiebo Luo. 2018

22 citations recorded by the source corpus.

Multi-task clustering improves the clustering performance of each task by transferring knowledge among the related tasks. An important aspect of multi-task clustering is to assess the task relatedness. However, to our knowledge, only two previous works have assessed the task relatedness, but they both have limitations. In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks. The objective function of MTCMRL consists of two parts: (1) within-task clustering: clustering each task by introducing linear regression model into symmetric nonnegative matrix factorization; (2) cross-task relatedness learning: updating the parameter of the linear regression model in each task by learning the model parameter relatedness between the clusters in each pair of tasks. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multi-task clustering methods.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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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. Mingsheng Long
  4. Jianmin Wang
  5. Dongrui Wu
  6. Sinno Jialin Pan
  7. Sebastian Ruder
  8. Philip S. Yu

In Computer Vision and Pattern Recognition

  1. Qiang Yang
  2. Yu Zhang
  3. Gabriella Csurka
  4. Dacheng Tao
  5. Michael R. Lyu
  6. Steffen Rendle

Citations in the corpus