Large-Scale Many-Class Learning

Abstract

A number of tasks, such as large-scale text categorization and word prediction, can benefit from efficient learning and classification when the number of classes (categories), in addition to instances and features, is large, that is, in the thousands and beyond. We investigate learning of sparse category indices to address this challenge. An index is a weighted bipartite graph mapping features to categories. On presentation of an instance, the index retrieves and scores a small set of candidate categories. The candidates can then be ranked and the ranking or the scores can be used for category assignment. We present novel online index learning algorithms. When compared to other approaches, including one-versus-rest and top-down learning and classification using support vector machines, we find that indexing is highly advantageous in terms of space and time efficiency, at both training and classification times, while yielding similar and often better accuracies. On problems with hundreds of thousands of instances and thousands of categories, the index is learned in minutes, while other methods can take orders of magnitude longer. As we explain, the design of the algorithm makes it convenient to maintain a constraint on the number of prediction connections a feature is allowed to make. This constraint is crucial in yielding efficient learning and classification.