Name
Abstract | ||
|---|---|---|
Most conventional methods of feature extraction do not pay much attention to the geometric properties of data, even in cases where the data have spatial features. In this study we introduce geometric algebra to undertake various kinds of feature extraction from spatial data. Geometric algebra is a generalization of complex numbers and of quaternions, and it is able to describe spatial objects and relations between them. This paper proposes to use geometric algebra to systematically extract geometric features from data given in a vector space. We show the results of classification of hand-written digits, which were classified by feature extraction with the proposed method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4634383 | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
Keywords | Field | DocType |
geometric algebra,data mining,feature extraction,gaussian distribution,algebra,spatial data,machine learning,quaternions,testing,gallium,computational geometry,image processing,multidimensional signal processing,vector space,vectors | Spatial analysis,Multidimensional signal processing,Vector space,Complex number,Pattern recognition,Computer science,Quaternion,Computational geometry,Feature extraction,Artificial intelligence,Geometric algebra,Machine learning | Conference |
ISSN | Citations | PageRank |
1098-7576 | 1 | 0.36 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minh Tuan Pham | 1 | 7 | 3.40 |
| Kanta Tachibana | 2 | 12 | 4.81 |
| Eckhard M. S. Hitzer | 3 | 8 | 1.61 |
| Sven Buchholz | 4 | 3 | 0.84 |
| Tomohiro Yoshikawa | 5 | 116 | 31.91 |
| Takeshi Furuhashi | 6 | 1 | 0.36 |