An new BSBL algorithm has been derived

A fast BSBL algorithm has been derived, and the work has been submitted to IEEE Signal Processing Letters.

Below is the paper:

Fast Marginalized Block SBL Algorithm
by Benyuan Liu, Zhilin Zhang, Hongqi Fan, Zaiqi Lu, Qiang Fu

The preprint can be downloaded at: http://arxiv.org/abs/1211.4909

Here is the abstract:
The performance of sparse signal recovery can be improved if both sparsity and correlation structure of signals can be exploited. One typical correlation structure is intra-block correlation in block sparse signals. To exploit this structure, a framework, called block sparse Bayesian learning (BSBL) framework, has been proposed recently. Algorithms derived from this framework showed promising performance but their speed is not very fast, which limits their applications. This work derives an efficient algorithm from this framework, using a  marginalized likelihood maximization method. Thus it can exploit block sparsity and intra-block correlation of signals. Compared to existing BSBL algorithms, it has close recovery performance to them, but has much faster speed. Therefore, it is more suitable for recovering large scale datasets.

Scientists See Promise in Deep-Learning Programs

The New York Times just has a report on  deep-learning. Some of the applications mentioned in the report  really refreshed my knowledge on it. Enjoy!

http://www.nytimes.com/2012/11/24/science/scientists-see-advances-in-deep-learning-a-part-of-artificial-intelligence.html?_r=0

Block Sparse Bayesian Learning (BSBL) has been accepted by IEEE Trans. on Signal Processing

Our work on Block Sparse Bayesian Learning (BSBL) has been accepted by IEEE Trans. on Signal Processing last week.

Here is the paper information:

Zhilin Zhang, Bhaskar. D. Rao, Extension of SBL Algorithms for theRecovery of Block Sparse Signals with Intra-Block Correlation, to appear in IEEE Trans. on Signal Processing

The preprint can be downloaded at: http://arxiv.org/abs/1201.0862
The codes can be downloaded at: https://sites.google.com/site/researchbyzhang/bsbl

Here is the abstract:

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 generalizing signals' block structure. We propose two families of algorithms based on the framework of block sparse Bayesian learning (BSBL). One family, directly derived from the BSBL framework, requires to know the block structure. Another family, derived from an expanded BSBL framework, is based on a weaker assumption on the block structure, and can be used in the case when the block structure is completely unknown. Using these algorithms we show that exploiting intra-block correlation is very helpful in improving recovery performance. These algorithms also shed light on how to modify existing algorithms or design new ones to exploit such correlation to improve performance.

The following are related application work:

Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao, Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning, IEEE Trans. Biomedical Engineering, 2012, accepted

Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao, Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware, IEEE Trans. Biomedical Engineering, vol.59, no.12, 2012