Zero-Shot Feature Selection via Transferring Supervised Knowledge
Zero-Shot Feature Selection via Transferring Supervised Knowledge
复制标题
通过转移监督知识进行零样本特征选择
DOI：
10.4018/ijdwm.2021040101
复制
发表时间：
2021
影响因子：
1.2
通讯作者：
Ye Xiaojun
中科院分区：
文献类型：
--
作者：
Wang Zheng;Wang Qiao;Zhao Tingzhang;Wang Chaokun;Ye Xiaojun
特征选择是一种有效的降维技术，在许多机器学习系统中起着重要的作用。监督知识可以显着提高性能。然而，面对新兴概念的快速增长，现有的监督方法可能很容易受到标记数据的稀缺性和有效性的影响。在本文中，作者研究了零炮特征选择问题（即，构建特征选择模型，该模型利用有限的“可见”概念的训练数据很好地推广到“不可见”概念）。具体来说，它们采用类语义描述（即，属性）作为特征选择的监督，以便利用从所看到的概念转移的监督知识。对于更可靠的判别特征，他们进一步提出了中心特征损失，鼓励所选特征捕捉所见概念的中心特征。在各种真实数据集上进行的大量实验证明了该方法的有效性。
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.