Instance weighting domain adaptation using distance kernel

Woojin Lee, Jaewook Lee, Saerom Park

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Domain adaptation methods aims to improve the accuracy of the target predictive classifier while using the patterns from a related source domain that has large number of labeled data. In this paper, we introduce new kernel weight domain adaptation method based on smoothness assumption of classifier. We propose new simple and intuitive method that can improve the learning of target data by adding distance kernel based cross entropy term in loss function. Distance kernel refers to a matrix which denotes distance of each instances in source and target domain. We efficiently reduced the computational cost by using the stochastic gradient descent method. We evaluated the proposed method by using synthetic data and cross domain sentiment analysis tasks of Amazon reviews in four domains. Our empirical results showed improvements in all 12 domain adaptation experiments.

Original languageEnglish
Pages (from-to)334-340
Number of pages7
JournalIndustrial Engineering and Management Systems
Volume17
Issue number2
DOIs
StatePublished - 2018

Keywords

  • Distance Kernel
  • Domain Adaptation
  • Sentimental Analysis

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