Search Machine Learning Repository: @inproceedings{icml2013_chen13e,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Fast Image Tagging},
    Url = {},
    Booktitle = {Proceedings of the 30th International Conference on Machine Learning (ICML-13)},
    Author = {Minmin Chen and Alice Zheng and Kilian Q. Weinberger},
    Number = {3},
    Month = may,
    Volume = {28},
    Editor = {Sanjoy Dasgupta and David Mcallester},
    Year = {2013},
    Pages = {1274-1282},
    Abstract = {Automatic image annotation is a difficult and highly relevant machine learning task. Recent advances have significantly improved the state-of-the-art in retrieval accuracy with algorithms based on nearest neighbor classification in carefully learned metric spaces. But this comes at a price of increased computational complexity during training and testing. We propose FastTag, a novel algorithm that achieves comparable results with two simple linear mappings that are co-regularized in a joint convex loss function. The loss function can be efficiently optimized in closed form updates, which allows us to incorporate a large number of image descriptors cheaply. On several standard real-world benchmark data sets, we demonstrate that FastTag matches the current state-of-the-art in tagging quality, yet reduces the training and testing times by several orders of magnitude and has lower asymptotic complexity.}