no code implementations • 3 Sep 2022 • Prakash B. Gohain, Magnus Jansson
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.
no code implementations • 17 Jun 2022 • Prakash B. Gohain, Magnus Jansson
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.
1 code implementation • 29 Aug 2017 • Martin Sundin, Arun Venkitaraman, Magnus Jansson, Saikat Chatterjee
We especially show how the constraint relates to the distributed consensus problem and graph Laplacian learning.
no code implementations • 23 Jan 2015 • Martin Sundin, Cristian R. Rojas, Magnus Jansson, Saikat Chatterjee
We develop latent variable models for Bayesian learning based low-rank matrix completion and reconstruction from linear measurements.
no code implementations • 12 Jan 2015 • Martin Sundin, Saikat Chatterjee, Magnus Jansson
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.
no code implementations • 30 Jun 2014 • Martin Sundin, Saikat Chatterjee, Magnus Jansson, Cristian R. Rojas
In this paper we develop a new Bayesian inference method for low rank matrix reconstruction.