Recursive NMF: Efficient label tree learning for large multi-class problems

Many object recognition or concept identification tasks require accurate detection of large number of classes. These applications present enormous challenges to traditional classification methods, which are mostly designed for solving problems with small number of classes. In this paper, we develop a method called recursive non-negative matrix factorization (RNMF) for building a hierarchical label tree over set of classes. The internal nodes of the tree employ linear classifiers to propagate a data instance to its corresponding leaf node, where one or more one class support vector machine (SVM) classifiers is applied to accurately predict its class. Our experiment results show that the proposed method achieves significant gain in test efficiency and comparable accuracy to some of the more expensive label tree learning methods.