Implicit application of polynomial filters in a k-step Arnoldi method
SIAM Journal on Matrix Analysis and Applications
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The symmetric eigenvalue problem
The symmetric eigenvalue problem
Clustering in large graphs and matrices
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Factorization with Uncertainty
International Journal of Computer Vision
Fast Monte-Carlo Algorithms for finding low-rank approximations
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Fast Monte-Carlo Algorithms for Approximate Matrix Multiplication
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
Density-weighted nyström method for computing large kernel eigensystems
Neural Computation
Clustered Nyström method for large scale manifold learning and dimension reduction
IEEE Transactions on Neural Networks
A Randomized Algorithm for Principal Component Analysis
SIAM Journal on Matrix Analysis and Applications
Fast Algorithms for Approximating the Singular Value Decomposition
ACM Transactions on Knowledge Discovery from Data (TKDD)
Stochastic algorithms in linear algebra: beyond the Markov chains and von Neumann-Ulam scheme
NMA'10 Proceedings of the 7th international conference on Numerical methods and applications
A fast solver for modeling the evolution of virus populations
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
DHCC: Divisive hierarchical clustering of categorical data
Data Mining and Knowledge Discovery
Sampling methods for the Nyström method
The Journal of Machine Learning Research
| Hi-index | 0.00 |
We demonstrate that an algorithm proposed by Drineas et. al. in [7] to approximate the singular vectors/values ofa matrix A, is not only oft heoretical interest but also a fast, viable alternative to traditional algorithms. The algorithm samples a small number ofro ws (or columns) oft he matrix, scales them appropriately to form a small matrix S and computes the singular value decomposition (SVD) of S, which is a good approximation to the SVD ofthe original matrix. We experimentally evaluate the accuracy and speed oft his randomized algorithm using image matrices and three different sampling schemes. Our results show that our approximations oft he singular vectors of A span almost the same space as the corresponding exact singular vectors of A.