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      Task-Driven Dictionary Learning

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          Abstract

          Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.

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          Most cited references 15

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          An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

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            Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

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              Single-Pixel Imaging via Compressive Sampling

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                Author and article information

                Journal
                27 September 2010
                2013-09-09
                Article
                10.1109/TPAMI.2011.156
                1009.5358
                1b90b888-8357-44d0-9635-377fec3686e3

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 4 (2012) 30
                final draft post-refereeing
                stat.ML
                ccsd

                Machine learning

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