Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Atomic Decomposition by Basis Pursuit
SIAM Review
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Protein classification with multiple algorithms
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
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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.