期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 26, 期 12, 页码 3287-3292出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2388782
关键词
Augmented quaternion statistics; mean square error (MSE); quaternion widely linear (WL) model; semi-WL (SWL) model
类别
资金
- Engineering and Physical Sciences Research Council [EP/H026266/1]
- Natural Science Foundation of Jiangsu Province [BK20140645]
- National Natural Science Foundation of China [61401094]
- Engineering and Physical Sciences Research Council [EP/H026266/1] Funding Source: researchfish
- EPSRC [EP/H026266/1] Funding Source: UKRI
The quaternion widely linear (WL) estimator has been recently introduced for optimal second-order modeling of the generality of quaternion data, both second-order circular (proper) and second-order noncircular (improper). Experimental evidence exists of its performance advantage over the conventional strictly linear (SL) as well as the semi-WL (SWL) estimators for improper data. However, rigorous theoretical and practical performance bounds are still missing in the literature, yet this is crucial for the development of quaternion valued learning systems for 3-D and 4-D data. To this end, based on the orthogonality principle, we introduce a rigorous closed-form solution to quantify the degree of performance benefits, in terms of the mean square error, obtained when using the WL models. The cases when the optimal WL estimation can simplify into the SWL or the SL estimation are also discussed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据