Globally Convergent Newton Algorithms for Blind Decorrelation
نویسندگان
چکیده
This paper presents novel Newton algorithms for the blind adaptive decorrelation of real and complex processes. They are globally convergent and exhibit an interesting relationship with the natural gradient algorithm for blind decorrelation and the Goodall learning rule. Indeed, we show that these two later algorithms can be obtained from their Newton decorrelation versions when an exact matrix inversion is replaced by an iterative approximation to it.
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