Sampling techniques for monte carlo matrix multiplication with applications to image processing

Authors:
Humberto Madrid;Valia Guerra;Marielba Rojas
Affiliations:
Center for Applied Mathematics Research (CIMA), Saltillo, Mexico;Institute of Cybernetics, Mathematics and Physics (ICIMAF), Havana, Cuba;Delft University of Technology, The Netherlands
Venue:
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Year:
2012

Citing 8
Cited 0

Implicit application of polynomial filters in a k-step Arnoldi method

SIAM Journal on Matrix Analysis and Applications
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Grouping Using the Nyström Method

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication

SIAM Journal on Computing
Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix

SIAM Journal on Computing
Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition

SIAM Journal on Computing
The Mailman algorithm: A note on matrix--vector multiplication

Information Processing Letters
Importance Sampling for a Monte Carlo Matrix Multiplication Algorithm, with Application to Information Retrieval

SIAM Journal on Scientific Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Randomized algorithms for processing massive data sets have shown to be a promising alternative to deterministic techniques. Sampling strategies are an essential aspect of randomized algorithms for matrix computations. In this work, we show that strategies that are effective or even optimal in the general case, can fail when applied to ill-conditioned matrices. Our experimental study suggests that there exists a relationship between sampling performance and conditioning of the matrices involved. We present an explanation for this behavior and propose a novel, efficient, and accurate sampling strategy for randomized multiplication of affinity matrices in image segmentation.