Transferable Feature Selection for Unsupervised Domain Adaptation: Extended Abstract

Yuguang Yan, Hanrui Wu, Yuzhong Ye, Chaoyang Bi, Min Lu, Dapeng Liu, Qingyao Wu, Michael K. Ng

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Domain adaptation aims at extracting knowledge from auxiliary source domains to assist the learning task in a target domain. Since the distributions of the source and target domains are different, directly using source data to build a classifier for the target domain may hamper the classification performance on the target data. In this paper, we propose to find a feature subset that is both transferable and discriminative, so that both the domain discrepancy and the classification loss measured on the selected features can be reduced. To achieve this, we formulate a new sparse learning model that is able to jointly reduce the domain discrepancy and select informative features for classification. Extensive experiments on real-world data sets demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
PublisherIEEE
Pages3855-3856
Number of pages2
ISBN (Electronic)9798350322279
ISBN (Print)9798350322286
DOIs
Publication statusPublished - 3 Apr 2023
Event39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States
Duration: 3 Apr 20237 Apr 2023
https://icde2023.ics.uci.edu/
https://ieeexplore.ieee.org/xpl/conhome/10184508/proceeding

Publication series

NameProceedings - International Conference on Data Engineering
Volume2023-April
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X

Competition

Competition39th IEEE International Conference on Data Engineering, ICDE 2023
Country/TerritoryUnited States
CityAnaheim
Period3/04/237/04/23
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

User-Defined Keywords

  • Domain Adaptation
  • Feature Selection
  • Sparse Learning Model

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