Friday, March 11, 2011

sparse




Samy Bengio
torch
What's Torch ?

It's a machine-learning library, written in simple C++ and distributed now under a BSD license.
Torch is currently developed at IDIAP, in Switzerland mountains.

PAMIR - Passive-Aggresive Model for Image Retrieval


PAMIR is a machine learning algorithm to learn a ranking function, i.e. a function which orders documents given a query. It has been primarily designed for multimodal retrieval, such as the retrieval of images from text queries. Its main advantages are scalability (it relies on online learning, which allows training from large datasets) and discriminative training (its training procedure optimizes a loss related to the final retrieval quality). Pamir is also a mountain range in Central Asia, but that's a different story...


Micael Elad Software

SVD

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics.

Ron Rubinstein

These toolboxes combine Matlab M-code with optimized MEX functions written in C. The source code is freely available for academic and personal use. The toolboxes are designed to be easy to use, and are fully documented - see the readme.txt file in each toolbox to get started. Also see the faq.txt file in each toolbox for some frequently asked questions. If you have any additional questions or comments, don't hesitate to contact me. Enjoy!


Yonina Eldar

Professor of Electrical Engineering

Visiting Professor at Stanford, 2009?10
Statistics and Electrical Engineering Departments

Areas of Interest


Sampling methods and A/D design
Compressed sensing
Detection and estimation theory
Optimization for signal processing
Signal processing and optimization for communication systems
Signal processing for optics
Computational biology




Michael Elad

Michael Elad is an Professor in the Computer Science Department of the Technion - Israel Institute of Technology. His fields of interest include signal processing, image processing, and computer vision; numerical analysis, numerical linear algebra and Machine learning algorithms. Follow the links at the top of the page to learn more...


Michal Aharon

Michal Aharon and Ron Kimmel : Representation Analysis and Synthesis of Lip Images Using Dimensionality Reduction (TR: CIS-2004-01).

M. Aharon, M. Elad, and A.M. Bruckstein, "The K-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation", to appear in the IEEE Trans. On Signal Processing.

M. Aharon, M. Elad, and A.M. Bruckstein, "On the Uniqueness of Overcomplete Dictionaries, and a Practical Way to Retrieve Them", Journal of Linear Algebra and Applications, Volume 416, Issue 1, Pages 48-67


M. Elad and M. Aharon, "Image Denoising Via Sparse and Redundant representations over Learned Dictionaries", to appear in the IEEE Trans. on Image Processing.

Conference papers:

M. Elad and M. Aharon, "Image denoising via learned dictionaries and sparse representation", CVPR, NY, June 17-22, 2006.

M. Aharon, M. Elad, and A.M. Bruckstein, ""The K-SVD Algorithm", Proceedings of SPARSE'05, Rennes, France, November 2005.

M. Aharon, M. Elad, and A.M. Bruckstein, "K-SVD and its non-negative variant for dictionary design", Proceedings of the SPIE conference wavelets, Vol. 5914, July 2005.


KSVD


Dave Donoho

Fifteen Years of Reproducible Research in Computational Harmonic Analysis
with Arian Maleki, Morteza Shahram, Victoria Stodden, and Inam Ur-Rahman.
Counting Faces of Randomly Projected Hypercubes and Orthants, with Applications
with Jared Tanner.
Exponential Bounds Implying Construction of Compressed Sensing Matrices, Error-Correcting Codes and Neighborly Polytopes by Random Sampling
with Jared Tanner.
Higher Criticism Thresholding: Optimal Feature Selection when Useful Features are Rare and Weak
with Jiashun Jin.
2007
Compressed Sensing MRI
with Michael Lustig and John Pauly.
Counting Faces of Randomly Projected Polytopes when the Projection Radically Lowers Dimension
with Jared Tanner.
2006
Fast Solution of L1 minimization problems when the solution may be sparse
with Yaacov Tsaig.
Sparse Solution of Underdetermined Linear Equations by Stagewise Orthogonal Matching Pursuit
with Yaakov Tsaig, Iddo Drori, and Jean-Luc Starck.