168 research outputs found
Fast Label Embeddings via Randomized Linear Algebra
Many modern multiclass and multilabel problems are characterized by
increasingly large output spaces. For these problems, label embeddings have
been shown to be a useful primitive that can improve computational and
statistical efficiency. In this work we utilize a correspondence between rank
constrained estimation and low dimensional label embeddings that uncovers a
fast label embedding algorithm which works in both the multiclass and
multilabel settings. The result is a randomized algorithm whose running time is
exponentially faster than naive algorithms. We demonstrate our techniques on
two large-scale public datasets, from the Large Scale Hierarchical Text
Challenge and the Open Directory Project, where we obtain state of the art
results.Comment: To appear in the proceedings of the ECML/PKDD 2015 conference.
Reference implementation available at https://github.com/pmineiro/randembe
Conditional Graphical Lasso for Multi-label Image Classification
© 2016 IEEE. Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correlations or lack the ability to learn image-dependent conditional label structures. In this paper, we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms
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