Page Views on Nuit Blanche since July 2010

Please join/comment on the Google+ Community (1843), the CompressiveSensing subreddit (1122), the Facebook page (123 likes), the LinkedIn Compressive Sensing group (3457) or the Advanced Matrix Factorization Group (1118)

Tuesday, March 20, 2012

Around the blogs in 80 hours

On the Advanced Matrix Factroization Group, a question just came up:

I've factored my problem currently to something like: p=Ax
A is a transform matrix relating them and I wish to find an x that results in a P matrix that has a somewhat sparse diagonal form. (elements primarily around diagonal in symmetric form)
The objective function has the same x variables but is say: b-Ax l2 norm.
I'm primarily interested in suggestions to constrain/get force the form of the solution.
In the Compressive Sensing Group, there were several questions:Does anybody knows if there is some International Conference/Workshop focused in CS topics and another on:Compressed Sensing/Sparse Reconstruction with Custom Dictionaries/Penalty Functions

On the blogs we have:

1 comment:

Anonymous said...

Hi igor,
This comment is related to the question above.
I remenber reading on your blog, about analysis sparsity. I hope this problem can be solved in a similar manner.
Objective function is to minimize b-Px l2 norm and the sparsity constraint is to be imposed on Ax. If the matrix Ax is having a sparse structure (can minimize nuclear norm), then you might solve the optimization problem:
minimize ||Ax||_nuclear subject to ||y-Px||_2<eps

or If the matrix Ax happens to be close to diagonal and has a sparse diagonal, then you cam minimize ||diag(Ax)||_1 in the place of nuclear norm also.