Informative Feature Selection for Domain Adaptation

IEEE Access

Zhihang Luo¹  Wenwen Gu²  Hanrui Wu³  Yuguang Yan³  Feng Sun³  Qing Du³ 

[1] Business School, The Hongkong University of Science and Technology, Hong Kong;School of Business, La Trobe University, Bundoora, VIC, Australia;School of Software Engineering, South China University of Technology, Guangzhou, China;

关键词: Domain adaptation;    feature selection;    structured multi-output learning;    transfer learning;   

DOI  :  10.1109/ACCESS.2019.2944226

来源: DOAJ

【 摘 要 】

Domain adaptation aims at extracting knowledge from an auxiliary source domain to assist the learning task in a target domain. When the data distribution of the target domain is different from that of the source domain, the direct use of source data for building a classifier for the target learning task cannot achieve promising performance. In this work, we propose a novel unsupervised domain adaptation method called Feature Selection for Domain Adaptation (FSDA), in which we aim to select a set of informative features. The benefits are two-fold. The first is to reduce the mismatch between the data distributions in the source and target domains by selecting a set of informative features in which they share similar properties. The second is to remove noisy features in the source domain such that the learning performance can be enhanced. We formulate a new sparse learning model for structured multiple outputs, including a vector to select informative features that can be used to jointly minimize the domain discrepancy and eliminate noisy features, and a classifier to perform prediction on the selected features. We develop a cutting-plane algorithm to solve the resulting optimization problem. Extensive experiments on real-world data sets are tested to demonstrate the effectiveness of the proposed method compared with the other existing methods.

【 授权许可】

Unknown