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Structure from motion (SfM) is a crucial and widely studied problem in computer vision. Recently, the factorization framework for SfM was formulated as a low rank approximation problem: the rank of rescaled measurement matrix is always smaller than four. Since the rank function is non-convex, a common practice is to replace with its convex surrogate, i.e., the nuclear norm. However, nuclear norm sometimes...
Sparse representation for machine learning has been exploited in past years. Several sparse representation based classification algorithms have been developed for some applications, for example, face recognition. In this paper, we propose an improved sparse representation based classification algorithm. Firstly, for a discriminative representation, a non-negative constraint of sparse coefficient is...
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold. The main idea is to formulate the dimensionality reduction problem as a classical...
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