Zero-Shot Feature Selection via Transferring Supervised Knowledge

Zheng Wang, Qiao Wang, Tingzhang Zhao, Chaokun Wang and Xiaojun Ye
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Zheng Wang: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China
Qiao Wang: School of Software, Tsinghua University, Beijing, China
Tingzhang Zhao: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China
Chaokun Wang: School of Software, Tsinghua University, Beijing, China
Xiaojun Ye: School of Software, Tsinghua University, Beijing, China

International Journal of Data Warehousing and Mining (IJDWM), 2021, vol. 17, issue 2, 1-20

Abstract: Feature selection, an effective technique for dimensionality reduction, plays an important role in many machine learning systems. Supervised knowledge can significantly improve the performance. However, faced with the rapid growth of newly emerging concepts, existing supervised methods might easily suffer from the scarcity and validity of labeled data for training. In this paper, the authors study the problem of zero-shot feature selection (i.e., building a feature selection model that generalizes well to “unseen” concepts with limited training data of “seen” concepts). Specifically, they adopt class-semantic descriptions (i.e., attributes) as supervision for feature selection, so as to utilize the supervised knowledge transferred from the seen concepts. For more reliable discriminative features, they further propose the center-characteristic loss which encourages the selected features to capture the central characteristics of seen concepts. Extensive experiments conducted on various real-world datasets demonstrate the effectiveness of the method.

Date: 2021
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