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Found 6 papers, 1 papers with code
Model Selection in High-Dimensional Block-Sparse Linear Regression
In this paper, we tackle the problem of model selection in a general linear regression where the parameter matrix possesses a block-sparse structure, i. e., the non-zero entries occur in clusters or blocks and the number of such non-zero blocks is very small compared to the parameter dimension.
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models
In this regard, extended BIC (EBIC), which is an extended version of the original BIC and extended Fisher information criterion (EFIC), which is a combination of EBIC and Fisher information criterion, are consistent estimators of the true model as the number of measurements grows very large.
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.
