Heterogeneous-neighborhood-based multi-task local learning algorithms

Yu ZHANG*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)

Abstract

All the existing multi-task local learning methods are defined on homogeneous neighborhood which consists of all data points from only one task. In this paper, different from existing methods, we propose local learning methods for multitask classification and regression problems based on heterogeneous neighborhood which is defined on data points from all tasks. Specifically, we extend the κ- nearest-neighbor classifier by formulating the decision function for each data point as a weighted voting among the neighbors from all tasks where the weights are task-specific. By defining a regularizer to enforce the task-specific weight matrix to approach a symmetric one, a regularized objective function is proposed and an efficient coordinate descent method is developed to solve it. For regression problems, we extend the kernel regression to multi-task setting in a similar way to the classification case. Experiments on some toy data and real-world datasets demonstrate the effectiveness of our proposed methods.

Original languageEnglish
JournalAdvances in Neural Information Processing Systems
Publication statusPublished - 2013
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: 5 Dec 201310 Dec 2013

Scopus Subject Areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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