Zero-shot Feature Selection via Exploiting Semantic Knowledge

Feature selection plays an important role in pattern recognition and machine learning systems. Supervised knowledge can significantly improve the performance. However, confronted with the rapid growth of newly-emerging concepts, existing supervised methods may easily suffer from the scarcity of labeled data for training. Therefore, this paper studies 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. To address this, inspired by zero-shot learning, we use class-semantic descriptions (i.e., attributes) which provide additional semantic information about unseen concepts as supervision. In addition, to seek for more reliable discriminative features, we further propose a novel loss function (named center-characteristic loss) which encourages the selected features to capture the central characteristics of seen concepts. Experimental results on three benchmarks demonstrate the superiority of the proposed method.