bio | website | vision.ai.uiuc.edu/~bghanem2 |
---|---|---|
location | Singapore | |
age | ||
visits | member for | 4 years, 9 months |
seen | Apr 6 '12 at 10:40 | |
stats | profile views | 95 |
Interested in research topics related to computer vision and machine learning, especially large scale optimization and randomization techniques for classification.
Sep 24 |
awarded | Autobiographer |
Jul 2 |
awarded | Curious |
Feb 2 |
awarded | Popular Question |
Mar 6 |
awarded | Popular Question |
Apr 6 |
revised |
conjugate function for matrix mixed norm
added 1 characters in body; edited body |
Apr 3 |
asked | conjugate function for matrix mixed norm |
Mar 29 |
comment |
Projected gradient descent for non-convex optimization problems
Thanks Brian for the comments! |
Mar 27 |
asked | Projected gradient descent for non-convex optimization problems |
Jan 7 |
comment |
smooth approximation of the hinge loss function
Sorry about the link. The paper can be viewed here: cs.uiuc.edu/homes/afarhad2/index_files/scene_discovery.pdf. Actually, I found the proper coefficients for the order 4 polynomial. The paper has a typo on page 5. Instead of $\frac{3}{2}x$ it should be $\frac{1}{2}x$. The link for the smooth quadratic hinge function was a great help. Thanks Suvrit. |
Jan 7 |
asked | smooth approximation of the hinge loss function |
Dec 27 |
comment |
Statistical estimation of singular values and vectors
@ J.M.: The matrix is an error matrix that varies in a complex way. There is dependence on other variables, so I can't model how the variation goes. I can say that the change is small, so can you refer me to the SVD update algorithms you had in mind? Thanks |
Dec 27 |
comment |
Statistical estimation of singular values and vectors
@ J.M.: The randomization techniques I referred to show that they are more efficient than the SVD power algorithm mentioned in the papers you suggested. |
Dec 27 |
comment |
Statistical estimation of singular values and vectors
Let me rephrase the question. Given an mxn matrix A, I need to compute the largest k singular values and vectors (U, $\Sigma$ and V). Are there any available randomized techniques that can estimate these values and vectors, whose computational complexity is better than the deterministic technique of SVD? I found this paper: cims.nyu.edu/~tygert/randsurvey.pdf. It shows 2 algorithms that can estimate the SVD but indirectly. It does so by first performing a randomized interpolative decomposition (ID). Does anyone know of a randomized technique that estimates SVD directly? |
Dec 27 |
asked | Statistical estimation of singular values and vectors |
Oct 15 |
awarded | Editor |
Oct 15 |
comment |
Maximum average value within a rectangular bounding box
i updated the question, so any extra comments would be appreciated. Thanks! |
Oct 15 |
comment |
Maximum average value within a rectangular bounding box
i updated the question, so any extra comments would be appreciated. Thanks! |
Oct 15 |
revised |
Maximum average value within a rectangular bounding box
added 495 characters in body |
Oct 14 |
awarded | Student |
Oct 14 |
asked | Maximum average value within a rectangular bounding box |