Add to ORKGFor entry sensing in matrix recovery (also known as matrix completion), we show that with high probability the Riemannian gradient descent and conjugate gradient descent methods based on the low rank matrix manifold are guaranteed to converge to the measured rank r matrix X ∈ Rn×n provided r ≤ C · σ 2 min (X) σ2max (X) ·
The low-rank matrix completion problem can be solved by Riemannian optimization on a fixed-rank mani...
Given a data matrix with partially observed entries, the low-rank matrix completion problem is one o...
In this article we present and discuss a two step methodology to find the closest low rank completio...
We establish theoretical recovery guarantees of a family of Riemannian optimization algorit...
We study the Riemannian optimization methods on the embedded manifold of low rank matrices ...
The matrix completion problem consists of finding or approximating a low-rank matrix based on a few ...
Low rank matrix recovery is a fundamental task in many real-world applications. The perfor-mance of ...
Low-rank matrix completion is the problem where one tries to recover a low-rank matrix from noisy ob...
Low rank matrix recovery is a fundamental task in many real-world applications. The perfor-mance of ...
We exploit the versatile framework of Riemannian optimization on quotient manifolds to develop R3MC,...
Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse pr...
Low-rank matrix completion is the problem of recovering the missing entries of a data matrix by usin...
We propose a new Riemannian geometry for fixed-rank matrices that is specifically tailored to the lo...
In this paper, we present modifications of the iterative hard thresholding (IHT) method for recovery...
Abstract. Matrices of low rank can be uniquely determined from fewer linear measurements, or entries...
The low-rank matrix completion problem can be solved by Riemannian optimization on a fixed-rank mani...
Given a data matrix with partially observed entries, the low-rank matrix completion problem is one o...
In this article we present and discuss a two step methodology to find the closest low rank completio...
We establish theoretical recovery guarantees of a family of Riemannian optimization algorit...
We study the Riemannian optimization methods on the embedded manifold of low rank matrices ...
The matrix completion problem consists of finding or approximating a low-rank matrix based on a few ...
Low rank matrix recovery is a fundamental task in many real-world applications. The perfor-mance of ...
Low-rank matrix completion is the problem where one tries to recover a low-rank matrix from noisy ob...
Low rank matrix recovery is a fundamental task in many real-world applications. The perfor-mance of ...
We exploit the versatile framework of Riemannian optimization on quotient manifolds to develop R3MC,...
Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse pr...
Low-rank matrix completion is the problem of recovering the missing entries of a data matrix by usin...
We propose a new Riemannian geometry for fixed-rank matrices that is specifically tailored to the lo...
In this paper, we present modifications of the iterative hard thresholding (IHT) method for recovery...
Abstract. Matrices of low rank can be uniquely determined from fewer linear measurements, or entries...
The low-rank matrix completion problem can be solved by Riemannian optimization on a fixed-rank mani...
Given a data matrix with partially observed entries, the low-rank matrix completion problem is one o...
In this article we present and discuss a two step methodology to find the closest low rank completio...