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Found 4 papers, 1 papers with code
A Connectedness Constraint for Learning Sparse Graphs
We especially show how the constraint relates to the distributed consensus problem and graph Laplacian learning.
Bayesian Learning for Low-Rank matrix reconstruction
We develop latent variable models for Bayesian learning based low-rank matrix completion and reconstruction from linear measurements.
Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine
Through simulations, we show the performance and computation efficiency of the new RVM in several applications: recovery of sparse and block sparse signals, housing price prediction and image denoising.
Relevance Singular Vector Machine for low-rank matrix sensing
In this paper we develop a new Bayesian inference method for low rank matrix reconstruction.
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