DocumentCode
2705960
Title
Learning averages over the lie group of symmetric positive-definite matrices
Author
Fiori, Simone ; Tanaka, Toshihisa
Author_Institution
Dipt. di Ing. Biomedica, Elettron. e Telecomun., Univ. Politec. delle Marche, Ancona, Italy
fYear
2009
fDate
14-19 June 2009
Firstpage
828
Lastpage
832
Abstract
In the present paper, we treat the problem of learning averages out of a set of symmetric positive-definite matrices (SPDMs). We discuss a possible learning technique based on the differential geometrical properties of the SPDM-manifold which was recently shown to possess a Lie-group structure under appropriate group definition. We first recall some relevant notions from differential geometry, mainly related to Lie-group theory, and then we propose a scheme of learning averages. Some numerical experiments will serve to illustrate the features of the learnt averages.
Keywords
Lie groups; computational complexity; differential geometry; estimation theory; group theory; learning (artificial intelligence); matrix algebra; Lie group theory; SPDM manifold; differential geometry; learning average; symmetric positive definite matrix; Automatic control; Biomedical engineering; Biomedical measurements; Biomedical signal processing; Covariance matrix; Humans; Intelligent control; Robotics and automation; Symmetric matrices; Tensile stress;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location
Atlanta, GA
ISSN
1098-7576
Print_ISBN
978-1-4244-3548-7
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2009.5178598
Filename
5178598
Link To Document