Abstract
A critical analysis of the canonical correlation analysis (CCA) approach in blind source separation (BSS) is provided. It is proved that by maximizing the autocorrelation functions of the recovered signals we can separate the source signals successfully. It is further shown that the CCA approach represents the same class of generalized eigenvalue decomposition (GEVD) problems as the matrix pencil method. Finally, online realizations of the CCA approach are discussed with a linear-predictor-based algorithm studied as an example.
| Original language | English |
|---|---|
| Pages (from-to) | 1505-1510 |
| Number of pages | 6 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 18 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - Sept 2007 |
Keywords
- Blind source separation (BSS)
- Canonical correlation analysis (CCA)
- Linear predictor
- Matrix pencil
- Second-order statistics (SOS)
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence
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