  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 matrices.
Our new representation has multiple advantages. Experiments on several vision
tasks demonstrate competitive performance with the state-of-the-art methods.
