57 citations · 64 across the 7 of their papers we have counts for
8 papers
A Connectedness Constraint for Learning Sparse Graphs
Martin Sundin, Arun Venkitaraman, Magnus Jansson +1
Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing. In some cases, the graph…
Bayesian Learning for Low-Rank matrix reconstruction
Martin Sundin, Cristian R. Rojas, Magnus Jansson +1
We develop latent variable models for Bayesian learning based low-rank matrix completion and reconstruction from linear measurements. For under-determined systems, the developed me…
Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine
Martin Sundin, Saikat Chatterjee, Magnus Jansson
Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or da…
Alternating Strategies Are Good For Low-Rank Matrix Reconstruction
Kezhi Li, Martin Sundin, Cristian R. Rojas +2
This article focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alte…
Relevance Singular Vector Machine for low-rank matrix sensing
Martin Sundin, Saikat Chatterjee, Magnus Jansson +1
In this paper we develop a new Bayesian inference method for low rank matrix reconstruction. We call the new method the Relevance Singular Vector Machine (RSVM) where appropriate p…
Alternating Least-Squares for Low-Rank Matrix Reconstruction
Dave Zachariah, Martin Sundin, Magnus Jansson +1
For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any…