Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IJCNN.2009.5178598 | IJCNN |
Keywords | Field | DocType |
appropriate group definition,numerical experiment,learnt average,present paper,lie-group structure,lie group,relevant notion,symmetric positive-definite matrix,differential geometry,differential geometrical property,possible learning technique,lie-group theory,data mining,symmetric matrices,covariance matrix,group theory,symmetric positive definite matrix,biomedical engineering,estimation theory,intelligent control,lie groups,manifolds,algebra,learning artificial intelligence,computational complexity,tensile stress,automatic control,estimation | Lie group,Group theory,Matrix (mathematics),Artificial intelligence,Manifold,Algebra,Positive-definite matrix,Pure mathematics,Symmetric matrix,Differential geometry,Machine learning,Mathematics,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Fiori | 1 | 494 | 52.86 |
| T. Tanaka | 2 | 638 | 95.91 |