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iPiano: Inertial proximal algorithm for nonconvex optimization
In this paper we study an algorithm for solving a minimization problem composed of a
differentiable (possibly nonconvex) and a convex (possibly nondifferentiable) function. The …
differentiable (possibly nonconvex) and a convex (possibly nondifferentiable) function. The …
Minimization of for Compressed Sensing
P Yin, Y Lou, Q He, J ** group sparsity
It is widely known that the total variation image restoration suffers from the stair casing
artifacts which results in blocky restored images. In this paper, we address this problem by …
artifacts which results in blocky restored images. In this paper, we address this problem by …
Reweighted sparse subspace clustering
Motion segmentation and human face clustering are two fundamental problems in computer
vision. The state-of-the-art algorithms employ the subspace clustering scheme when …
vision. The state-of-the-art algorithms employ the subspace clustering scheme when …
Sparse Lq-norm least squares support vector machine with feature selection
Least squares support vector machine (LS-SVM) is a popular hyperplane-based classifier
and has attracted many attentions. However, it may suffer from singularity or ill-condition …
and has attracted many attentions. However, it may suffer from singularity or ill-condition …
Combined higher order non-convex total variation with overlap** group sparsity for impulse noise removal
A typical approach to eliminate impulse noise is to use the ℓ 1-norm for both the data fidelity
term and the regularization terms. However, the ℓ 1-norm tends to over penalize signal …
term and the regularization terms. However, the ℓ 1-norm tends to over penalize signal …