Heterogeneous feature space based task selection machine for unsupervised transfer learning

Shan Xue*, Jie Lu, Guangquan Zhang, Li Xiong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

Transfer learning techniques try to transfer knowledge from previous tasks to a new target task with either fewer training data or less training than traditional machine learning techniques. Since transfer learning cares more about relatedness between tasks and their domains, it is useful for handling massive data, which are not labeled, to overcome distribution and feature space gaps, respectively. In this paper, we propose a new task selection algorithm in an unsupervised transfer learning domain, called as Task Selection Machine (TSM). It goes with a key technical problem, i.e., feature mapping for heterogeneous feature spaces. An extended feature method is applied to feature mapping algorithm. Also, TSM training algorithm, which is main contribution for this paper, relies on feature mapping. Meanwhile, the proposed TSM finally meets the unsupervised transfer learning requirements and solves the unsupervised multi-task transfer learning issues conversely.

Original languageEnglish
Title of host publicationProceedings, The 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages46-51
Number of pages6
ISBN (Electronic)9781467393225, 9781467393232
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015 - Taipei, Taiwan
Duration: 24 Nov 201527 Nov 2015

Conference

Conference10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015
CountryTaiwan
CityTaipei
Period24/11/1527/11/15

Keywords

  • feature mapping
  • heterogeneous feature space
  • multi-task learning
  • transfer learning
  • unsupervised learnig

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