2013
ICCV
ICCV 2013
Non-convex P-Norm Projection for Robust Sparsity
Abstract
In this paper, we investigate the properties of L p norm (p Ittiswithin a projection framework. We start with the KKT equations of the non-linear optimization problem and then use its key properties to arrive at an algorithm for L p norm projection on the non-negative simplex. We compare with L 1 projection which needs prior knowledge of the true norm, as well as hard thresholding based sparsification proposed in recent compressed sensing literature. We show performance improvements compared to these techniques across different vision applications.
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Conference Pioneer
— ICCV 2013
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— simplex projection
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Hot Topic Early Bird
— non-convex optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio