Matrix Recipes for Hard Thresholding Methods
نویسندگان
چکیده
منابع مشابه
Normalized Iterative Hard Thresholding for Matrix Completion
Matrices of low rank can be uniquely determined from fewer linear measurements, or entries, than the total number of entries in the matrix. Moreover, there is a growing literature of computationally efficient algorithms which can recover a low rank matrix from such limited information, typically referred to as matrix completion. We introduce a particularly simple yet highly efficient alternatin...
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The matrix completion problem is to reconstruct an unknown matrix with low-rank or approximately low-rank constraints from its partially known samples. Most methods to solve the rank minimization problem are relaxing it to the nuclear norm regularized least squares problem. Recently, there have been some simple and fast algorithms based on hard thresholding operator. In this paper, we propose a...
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The use of M-estimators in generalized linear regression models in high dimensional settings requires risk minimization with hard L0 constraints. Of the known methods, the class of projected gradient descent (also known as iterative hard thresholding (IHT)) methods is known to offer the fastest and most scalable solutions. However, the current state-of-the-art is only able to analyze these meth...
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In this paper we consider l0 regularized convex cone programming problems. In particular, we first propose an iterative hard thresholding (IHT) method and its variant for solving l0 regularized box constrained convex programming. We show that the sequence generated by these methods converges to a local minimizer. Also, we establish the iteration complexity of the IHT method for finding an -loca...
متن کاملOn Accelerated Hard Thresholding Methods for Sparse Approximation
We propose and analyze acceleration schemes for hard thresholding methods with applications to sparse approximation in linear inverse systems. Our acceleration schemes fuse combinatorial, sparse projection algorithms with convex optimization algebra to provide computationally efficient and robust sparse recovery methods. We compare and contrast the (dis)advantages of the proposed schemes with t...
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ژورنال
عنوان ژورنال: Journal of Mathematical Imaging and Vision
سال: 2013
ISSN: 0924-9907,1573-7683
DOI: 10.1007/s10851-013-0434-7