Sparse Representation: Extract Adaptive Neighborhood for Multilabel Classification

Abstract

Unlike traditional classification tasks, multilabel classification allows a sample to associate with more than one label. This generalization naturally arises the difficulty in classification. Similar to the single label classification task, neighborhood-based algorithms relying on the nearest neighbor have attracted lots of attention and some of them show positive results. In this paper, we propose an Adaptive Neighborhood algorithm for multilabel classification. Constructing an adaptive neighborhood is challenging because specified information about the neighborhood, e.g. similarity measurement, should be determined automatically during construction rather than provided by the user beforehand. Few literature has covered this topic and we address this difficulty by solving an optimization problem based on the theory of sparse representation. Taking advantage of the extracted adaptive neighborhood, classification can be readily done using weighted sum of labels of training data. Extensive experiments show our proposed method outperforms the state-of-the-art.