  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.
