Abstract
In this chapter, some concepts which are extensions of sparse properties are outlined. First, it describes some works about low-rank matrix approximation which include \(\ell _2\)-norm Wiberg algorithm and \(\ell _1\)-norm Wiberg algorithm. Second, it outlines the graphical models in compressed sensing which include message passing algorithm and approximate message passing algorithm. Third, it describes collaborative representation-based classifiers which use the \(\ell _2\)-norm to replace the \(\ell _1\)-norm in sparse representation model and the collaborative representation-based classifier (CRC). Lastly, it also describes some knowledge about high-dimensional nonlinear learning which includes kernel sparse representation, anchor points approaches, and sparse manifold learning.
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Cheng, H. (2015). Beyond Sparsity. In: Sparse Representation, Modeling and Learning in Visual Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6714-3_9
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DOI: https://doi.org/10.1007/978-1-4471-6714-3_9
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