Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.

This blog reports latest progresses in
(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home
(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures
(3) My Works

Wednesday, August 17, 2011

Look for more compressed sensing algorithms for cluster-structured sparse signals

I am now deriving some algorithms for cluster-structured sparse signals (and block-sparse signals). I plan to do some experiments, comparing mine with existing algorithms. Generally, my algorithms do not need any information about the cluster size, cluster number, cluster partition, etc. So, my algorithms can be used to compare most, if not all, existing algorithms. However, currently, I only compared those classic algorithms, such as group Lasso, overlap group Lasso, DGS, BCS-MCMC, block OMP (and its variants -- I don't know why, these OMP algorithms are very poor, especially in noisy cases). Although there are branch of papers proposed  state-of-the-art algorithms, their codes are not available online. If you, my dear readers, happen to know some good algorithms (and their codes are available online), please let me know. Thank you.


  1. Have you looked at SPAMs :


  2. Thank you, Igor. I heard the SPAM, but I didn't know it can solve the structured sparse problems. I will try it soon. Thanks again.