6 papers with code ยท
Methodology

Subtask of
Matrix Completion

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The proposed approach maintains smoothness across the matrix, produces accurate estimates of the missing data, converges iteratively, and it is computationally tractable with a controllable upper bound on the number of iterations until convergence.

IMAGE RECONSTRUCTION LOW-RANK MATRIX COMPLETION MULTI-TASK LEARNING RECOMMENDATION SYSTEMS TIME SERIES

This paper considers the problem of estimating a low-rank matrix from the observation of all, or a subset, of its entries in the presence of Poisson noise.

We propose an algorithm for low rank matrix completion for matrices with binary entries which obtains explicit binary factors.

As a real scenes sensing approach, depth information obtains the widespread applications.

Specifically, for an arbitrary Riemannian manifold $\mathcal{M}$ of dimension $d$, a sufficiently smooth (possibly non-convex) objective function $f$, and under weak conditions on the retraction chosen to move on the manifold, with high probability, our version of PRGD produces a point with gradient smaller than $\epsilon$ and Hessian within $\sqrt{\epsilon}$ of being positive semidefinite in $O((\log{d})^4 / \epsilon^{2})$ gradient queries.

Low-rank matrix completion (LRMC) is a classical model in both computer vision (CV) and machine learning, and has been successfully applied to various real applications.

In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches.

This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently.

The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios.

Our work aims to fill a critical gap in the literature by generalizing parallel inference algorithms to optimization on manifolds.