Efficient Discriminative Learning of Class Hierarchy for Many Class Prediction

Abstract

Recently the maximum margin criterion has been employed to learn a discriminative class hierarchical model, which shows promising performance for rapid multi-class prediction. Specifically, at each node of this hierarchy, a separating hyperplane is learned to split its associated classes from all of the corresponding training data, leading to a time-consuming training process in computer vision applications with many classes such as large-scale object recognition and scene classification. To address this issue, in this paper we propose a new efficient discriminative class hierarchy learning approach for many class prediction. We first present a general objective function to unify the two state-of-the-art methods for multi-class tasks. When there are many classes, this objective function reveals that some classes are indeed redundant. Thus, omitting these redundant classes will not degrade the prediction performance of the learned class hierarchical model. Based on this observation, we decompose the original optimization problem into a sequence of much smaller sub-problems by developing an adaptive classifier updating method and an active class selection strategy. Specifically, we iteratively update the separating hyperplane by efficiently using the training samples only from a limited number of selected classes that are well separated by the current separating hyperplane. Comprehensive experiments on three large-scale datasets demonstrate that our approach can significantly accelerate the training process of the two state-of-the-art methods while achieving comparable prediction performance in terms of both classification accuracy and testing speed.