890 citations · 3k across the 39 of their papers we have counts for
6 papers · 1 filter
Multimodal Sparse Bayesian Dictionary Learning
Igor Fedorov, Bhaskar D. Rao
This paper addresses the problem of learning dictionaries for multimodal datasets, i.e. datasets collected from multiple data sources. We present an algorithm called multimodal spa…
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
Igor Fedorov, Alican Nalci, Ritwik Giri +3
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered…
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning
Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig +1
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambul…
Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation
Zhilin Zhang, Bhaskar D. Rao
We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting signals' intra-block correlation and the other by generalizi…
Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors
Zhilin Zhang, Bhaskar D. Rao
Iterative reweighted algorithms, as a class of algorithms for sparse signal recovery, have been found to have better performance than their non-reweighted counterparts. However, fo…
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Zhilin Zhang, Bhaskar D. Rao
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlate…