Lp Norm Relaxation Approach for Large Scale Data Analysis: A Review

In the last few years there has been a lot of attention to the techniques of sparse representation and low-rank approximation. These techniques have shown important applications in many areas in image and video processing, signal analysis, computer vision and machine learning. These techniques can also be used in unsupervised and dictionary learning to uncover high-order relations in the data and to train deep neural networks. This paper is a review to discuss the latest developments and show the effectiveness of these techniques in large scale data processing problems.

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