Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.
Source: Universal Matrix Completion
In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.
The proposed low gradient regularization is integrated with the low rank regularization into the low rank low gradient approach for depth image inpainting.
In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation.
Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.
Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space.
Ranked #1 on Recommendation Systems on Flixster (using extra training data)
The main challenge with this strategy is the high computational complexity of matrix completion.
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.
In this work, we show that a simple modification of our robust ST solution also provably solves ST-miss and robust ST-miss.
We consider the problem of reconstructing a low-rank matrix from a small subset of its entries.