Questions and some Answers on the LinkedIn Groups (Compressive Sensing and Advanced Matrix Factorizations)

Here is a sample of questions and some answers that appeared on the compressive sensing and matrix factorization groups on LinkedIn.

Recently on The Advanced Matrix Factorization Group ( 890 members )

• John asks a question about the mathematical dual of the SVD he also pointed our attention on Butterfly Decompositions for Arbitrary Unitary Matrices
• German asked a question Regarding rank constraints in principal components pursuit

Recently on the Compressive Sensing Study Group ( 2,904 members )

• Matthieu asked about large scale computations of L1 minimization
• Rohit asked about the Computational complexity of reconstruction (which I think is for hardware)Patrick wonders about IST and AMP phase transitions
• Mrinal wonders about the the value of the constant in compressed sensing?
• Aleksandar wonders about a good implementation of the K-SVD algorithm in C/C++ for windows or clean portable...
• Zhijin wonders about a problem about the difference between CS and low-rank matrix completion
• Matija wonders about what the Powerful CS application fields are..
• WanChien wonders about Non-negative Sparse Recovery
• I wondered if Compressive Holography (was) really **that** compressive ?
• Mihai-Alexandru wondered about Maximum-entropy (recovery) method versus Compressive Sensing
• Misha wondered about if Anyone knew of a (hopefully good) reference for doing compressed sensing type reconstruction with an unknown sensing matrix?
• but to me the most interesting question is by Xuyang: Since dictionary learning is a sparse representation, is it proper to used in one-pixel imaging as a dictionary during the reconstruction?

This image was taken by Rear Hazcam: Right B (RHAZ_RIGHT_B) onboard NASA's Mars rover Curiosity on Sol 635 (2014-05-20 14:52:29 UTC).

Image Credit: NASA/JPL-Caltech

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