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The PCA dimensionality reduction algorithm for 2D data with the Laplacian noise model, i.e., L1-2DPCA, not only preserves the structural relation among 2D data, but also is robust for data outliers. The algorithm relies on the EM algorithm with great computational cost. In order to learn intrinsic information more consistently, this paper takes a view of manifold optimization for the model based on...
Restricted Boltzmann Machine (RBM) is a particular type of random neural network models modeling vector data based on the assumption of Bernoulli distribution. For multidimensional and non-binary data, it is necessary to vectorize and discretize the information in order to apply the conventional RBM. It is well-known that vectorization would destroy internal structure of data, and the binary units...
A compressive light field camera architecture has been proposed to recover light fields from a single image. This technique has recently gained increasing interest. The reconstruction quality of light field depends on the incoherence of a projection matrix and dictionary. Compressive light field acquisition is different from acquiring traditional signal while the projection matrix has specific structural...
Image matting refers to the problem of accurately extracting foreground objects in images and video. The most recent work by Levin, Lischinski and Weiss (2008) in natural image matting relies on the local smoothness assumptions on foreground and background colors on which a cost function is established. The closed-form solution has been derived based on certain degree of user inputs. In this paper,...
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