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| July 28
| July 28
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| Zhuolin Jiang
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| Discriminative Dictionary Learning for Sparse Representation
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| August 4
| August 4
Robust regression is a combinatorial optimization problem. Hence, algorithms such as RANSAC and least median squares (LMedS), which are successful in solving low-dimensional problems, can not be used for solving high-dimensional problems. We show that under certain conditions the robust linear regression problem can be solved accurately using polynomial-time algorithms such as a modified version of basis pursuit and a sparse Bayesian algorithm. We then extend our robust formulation to the case of kernel regression, specifically to propose a robust version for relevance vector machine (RVM) regression.
Robust regression is a combinatorial optimization problem. Hence, algorithms such as RANSAC and least median squares (LMedS), which are successful in solving low-dimensional problems, can not be used for solving high-dimensional problems. We show that under certain conditions the robust linear regression problem can be solved accurately using polynomial-time algorithms such as a modified version of basis pursuit and a sparse Bayesian algorithm. We then extend our robust formulation to the case of kernel regression, specifically to propose a robust version for relevance vector machine (RVM) regression.
===Discriminative Dictionary Learning for Sparse Representation===
Speaker: [http://www.umiacs.umd.edu/~zhuolin/ Zhuolin Jiang] -- Date: July 28, 2011
Sparse coding approximates an input signal by a sparse linear combination of items from an over-complete dictionary. The sparse coding-based approaches lead to state-of-the-art results for many signal or image processing tasks and advances in computer vision tasks such as object recognition. However, the performance of sparse coding relies on the quality of dictionary. How to design or learn the best dictionary adapted to natural signals has been the topic of much research in the past. In this talk I will first introduce some recent techniques that learn the dictionary from training data. Next I will present a label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse representation. It yields dictionaries so that feature points with the same class labels have similar sparse codes.
