Name
Abstract | ||
|---|---|---|
In this paper, we study the blind source separation (BSS) problem of temporally correlated signals via exploring the nonlinear temporal structure and high-order statistics of source signals. A BSS method based on the nonlinear predictability of original sources is proposed, which extends linear coding complexity used by the original complexity pursuit to nonlinear coding complexity. Simulations by nonstationarity sources verify the efficient implementation of the proposed method, especially its robustness to the outliers. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.650 | ICNC |
Keywords | Field | DocType |
bss method,temporally correlated signals,linear codes,original source,blind source separation (bss),generalized complexitypursuit algorithm (gcp),encoding,nonstationarity source,generalized complexity pursuit,blind source separation,nonlinear predictability,linear coding complexity,coding complexity,original complexity pursuit,nonlinear temporal structure,correlation,linear code,prediction algorithms | Predictability,Nonlinear system,Pattern recognition,Computer science,Outlier,Robustness (computer science),Coding (social sciences),Artificial intelligence,Blind signal separation,Source separation,Machine learning,Encoding (memory) | Conference |
Volume | ISBN | Citations |
3 | 978-0-7695-3304-9 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenwei Shi | 1 | 559 | 63.11 |
| Zhiguo Jiang | 2 | 321 | 45.58 |
| Jihao Yin | 3 | 90 | 12.18 |