Highly Scalable Matching Pursuit Signal Decomposition Algorithm
Shared by Ashok Srivastava, updated on Sep 22, 2010
Summary
- Author(s) :
- Dan Christensen, Santanu Das, Ashok Srivastava
- Abstract
In this research, we propose a variant of the classical Matching Pursuit Decomposition (MPD) algorithm with significantly improved scalability and computational performance. MPD is a powerful iterative algorithm that decomposes a signal into linear combinations of its dictionary elements or “atoms”. A best fit atom from an arbitrarily defined dictionary is determined through cross-correlation. The selected atom is subtracted from the signal and this procedure is repeated on the residual in the subsequent iterations until a stopping criteria is met.
A sufficiently large dictionary is required for an accurate reconstruction; this in return increases the computational burden of the algorithm, thus limiting its applicability and level of adoption. Our main contribution lies in improving the computational efficiency of the algorithm to allow faster decomposition while maintaining a similar level of accuracy. The Correlation Thresholding and Multiple Atom Extractions techniques were proposed to decrease the computational burden of the algorithm. Correlation thresholds prune insignificant atoms from the dictionary. The ability to extract multiple atoms within a single iteration enhanced the effectiveness and efficiency of each iteration. The proposed algorithm, entitled MPD++, was demonstrated using real world data set.
- Publication Name
- International Workshop on Structural Health Management
- Publication Location
- Proceedings of the International Workshop on Structural Health Management
- Year Published
- 2009
Files
Discussions
Ashok's Projects (16)
-
-
-
Vehicle Level Reasoning System-VLRS
5 members
-
Need help?
Visit our help center