Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics
Xiyang Dai,
Sameh Khamis,
Yangmuzi Zhang
et al.
Abstract:Sparse representations have been successfully applied to signal processing, computer vision and machine learning. Currently there is a trend to learn sparse models directly on structure data, such as region covariance. However, such methods when combined with region covariance often require complex computation. We present an approach to transform a structured sparse model learning problem to a traditional vectorized sparse modeling problem by constructing a Euclidean space representation for region covariance … Show more
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