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
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.
On the blogs we have:
- Vladimir mentions the Samsung Call to Universities (one of the subject is camera 2.0)
- Patrick is hoping there will be a camera at the GraphLab workshop
- Bob talks Music genre recognition: On the way to answers,
- SMC2012 3rd call for papers/posters/demos
- Paper of the Day (Po'D): Implementations of Orthogonal Matching Pursuit Edition
- Dirk writes about Some Sloan-Fellowships 2012 related to sparsity and signal processing
- Danny talks about How to prepare your problem to be used in GraphLab / GraphLab parsers
- Zhilin talks about The future of FMRI connectivity and Performance measurement index for compressed sensing of structured signals (an item discussed here and here before)
- Anna has her Lectures 10 and 11 of M651 on her blog
- Alex has a Reading List for Feb and March 2012
- The SAGE blog talks about integration of Python3 and Sage
- Christian talks about simulated annealing for Sudokus [2]
- Muthu mentions two Big Data workshops.
- Djalil talks about A random walk on the unitary group, Brownian Motion and From seductive theory to concrete applications (which got me thinking about writing this entry: Whose heart doesn't sink at the thought of Dirac being inferior to Theora ?)
- John mentions the The Submodularity workshop and Lucca Professorship
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1 comment:
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.
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