Titlebook: Dictionary Learning in Visual Computing; Qiang Zhang,Baoxin Li Book 2015 Springer Nature Switzerland AG 2015

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https://doi.org/10.1007/978-94-010-3601-6d for various types of applications. The examples and discussion in the previous chapters only serve to illustrate a fraction of the diversity and general applicability of sparse representation and its various algorithms. In this chapter, we use a well-studied application, face recognition, as a cas

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Two Models of Foundation in the ,or how they are typically used: learning dictionary for sparse representation (Sec. 3.1), learning dictionary for classification tasks (Sec. 3.2), joint learning of multiple dictionaries (Sec. 3.3), on-line dictionary learning (Sec. 3.4), and statistical dictionary learning (Sec. 3.5).

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Husserl in Contemporary Contexte corresponding solution will also vary. In this chapter, we discuss several common ways of formulating the sparse learning problem, along with basic ideas behind the solutions for these formulations. The details of various algorithms for solving the learning problem are to be elaborated in the next chapter.

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Fundamental Computing Tasks in Sparse Representation,e corresponding solution will also vary. In this chapter, we discuss several common ways of formulating the sparse learning problem, along with basic ideas behind the solutions for these formulations. The details of various algorithms for solving the learning problem are to be elaborated in the next chapter.