Material Detail
Structured Low-Rank Approximation as Optimization on a Grassmann Manifold
This video was recorded at International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS): theory and applications, Leuven 2013. Many data modeling problems can be posed and solved as a structured low-rank approximation problem. Using the variable projection approach, the problem is reformulated as optimization on a Grassmann manifold. We compare local optimization methods based on different parametrizations of the manifold, including recently proposed penalty method and method of switching permutations. A numerical example of system identification is provided.
Quality
- User Rating
- Comments
- Learning Exercises
- Bookmark Collections
- Course ePortfolios
- Accessibility Info
More about this material
- Mobile Compatibility:
- Not specified at this time
- Language:
- English
- Cost Involved:
- No
- Source Code Available:
- No
- Accessibility Information Available:
- Unknown
- Creative Commons:
-
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
