The present research work proposes a new fast fixed-point average-value learning algorithm on the compact Stiefel manifold based on a mixed retraction/lifting pair. Numerical comparisons between fixed-point algorithms based on the proposed non-associated retraction/lifting map pair and two associated retraction/lifting pairs confirm that the averaging algorithm based on a combination of mixed maps is remarkably less computationally demanding than the same averaging algorithm based on any of the constituent associated retraction/lifting pairs.
Mixed maps for learning a Kolmogoroff-Nagumo-type average element on the compact Stiefel manifold / Fiori, Simone; T., Kaneko; T., Tanaka. - ELETTRONICO. - (2014), pp. 4518-4522. [10.1109/ICASSP.2014.6854457]
Mixed maps for learning a Kolmogoroff-Nagumo-type average element on the compact Stiefel manifold
FIORI, Simone;
2014-01-01
Abstract
The present research work proposes a new fast fixed-point average-value learning algorithm on the compact Stiefel manifold based on a mixed retraction/lifting pair. Numerical comparisons between fixed-point algorithms based on the proposed non-associated retraction/lifting map pair and two associated retraction/lifting pairs confirm that the averaging algorithm based on a combination of mixed maps is remarkably less computationally demanding than the same averaging algorithm based on any of the constituent associated retraction/lifting pairs.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.