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Task-driven dictionary learning (2012).
| Content Provider | CiteSeerX |
|---|---|
| Author | Mairal, Julien Bach, Francis Ponce, Jean |
| 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. |
| File Format | |
| Publisher Date | 2012-01-01 |
| Access Restriction | Open |
| Subject Keyword | Task-driven Dictionary Learning Sparse Representation Corresponding Optimization Problem Large-scale Setting Regression Task Supervised Dictionary Learning Nonlinear Inverse Image Problem Dictionary Amount Image Classification Machine Learning Handwritten Digit Classification Supervised Way Natural Image Large-scale Matrix Factorization Problem Signal Processing Much Recent Research General Formulation Linear Combination Wide Variety Semi-supervised Classification Classical Optimization Tool Digital Art Identification Efficient Algorithm |
| Content Type | Text |
