| Paper: | MLSP-L3.4 | ||
| Session: | Learning Theory II | ||
| Time: | Thursday, May 18, 11:00 - 11:20 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Bayesian Learning and Modeling | ||
| Title: | BAYESIAN L1-NORM SPARSE LEARNING | ||
| Authors: | Yuanqing Lin, Daniel D. Lee, University of Pennsylvania, United States | ||
| Abstract: | We propose a Bayesian framework for learning the optimal regularization parameter in the L1-norm penalized east-mean-square (LMS) problem, also known as LASSO or basis pursuit. The setting of the regularization parameter is critical for deriving a correct solution. In most existing methods, the scalar regularization parameter is often determined in a heuristic manner; in contrast, our approach infers the optimal regularization setting under a Bayesian framework. Furthermore, Bayesian inference enables an independent regularization scheme where each coefficient (or weight) is associated with an independent regularization parameter. Simulations illustrate the improvement using our method in discovering sparse structure from noisy data. | ||
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