Name
Abstract | ||
|---|---|---|
Inspired by recent work in compressive sensing, we propose a framework for the detection of stochastic signals from optimized projections. In order to generate a good projection matrix, we use dimensionality reduction techniques based on the maximization of the mutual information between the projected signals and their corresponding class labels. In addition, classification techniques based on Support Vector Machines (SVMs) are applied for the final decision process. Simulation results show that the realizations of the stochastic process are detected with higher accuracy and lower complexity than a scheme per-forming signal reconstruction first, followed by detection based on the reconstructed signal. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CISS.2008.4558656 | 2008 42ND ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS, VOLS 1-3 |
Keywords | Field | DocType |
projection matrix,signal processing,svm,signal reconstruction,noise,support vector machine,gaussian noise,compressed sensing,awgn,optimization,noise measurement,signal detection,stochastic processes,stochastic process,mutual information,support vector machines | Signal processing,Dimensionality reduction,Detection theory,Pattern recognition,Computer science,Support vector machine,Stochastic process,Artificial intelligence,Mutual information,Signal reconstruction,Compressed sensing | Conference |
Citations | PageRank | References |
6 | 0.47 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José-emilio Vila-forcén | 1 | 19 | 2.29 |
| Antonio Artés-Rodríguez | 2 | 206 | 34.76 |
| Javier Garcia-Frias | 3 | 716 | 69.63 |