Clustered Multi-task Feature Learning for Attribute Prediction

Semantic attributes have been proposed to bridge the semantic gap between low-level feature representation and high-level semantic understanding of visual objects. Obtaining a good representation of semantic attributes usually requires learning from high-dimensional low-level features, which often suffers from the curse of dimensionality. Designing a good feature-selection approach would benefit attribute prediction and in turn its related applications. Since semantic attributes of an object are usually “related”, in the literature multi-task learning has been introduced for multi-attribute prediction, either by assuming that all attributes are somehow correlated or by manually dividing attributes into related groups. However, the performance of such approaches greatly rely on the task structure. The prediction performance would degrade if the assumed task structure does not match to that of the problem. Desired is an approach that can automatically detect problem-specific clustering structures of the attributes. In this paper, we propose a novel clustered multi-task feature selection approach utilizing K-means and group sparsity regularizers, and develop an efficient alternating optimization algorithm. Experiments demonstrate that the proposed approach can automatically capture the task structure and hence result in obvious performance gain in attribute prediction, when compared with existing state-of-the-art approaches.