Papers › Bregman Divergence Based Approach for Adaptive System Identification and Line Enhancement
Bregman Divergence Based Approach for Adaptive System Identification and Line Enhancement
Parth Sharma, Pyari Mohan Pradhan
This article puts forth a novel class of least mean square (LMS) techniques, including beta divergence-inspired LMS (BLMS), Itakura-Saito divergence-inspired LMS (ISBLMS), and Kullback-Leibler divergence-inspired LMS (KLLMS). The mentioned divergence measures are part of the Bregman divergence category of information-theoretic divergence. Mean and mean-square analysis for the proffered class of algorithms is derived to find the bound on the learning-rate for stable convergence. The effectiveness of the introduced class of algorithms is showcased for the application of time-varying system identification and adaptive line enhancement.
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