Name
Title | ||
|---|---|---|
A Comparative Study Of Supervised Learning Algorithms For Symmetric Positive Definite Features |
Abstract | ||
|---|---|---|
In recent years, the use of Riemannian geometry has reportedly shown an increased performance for machine learning problems whose features lie in the symmetric positive definite (SPD) manifold. The present paper aims at reviewing several approaches based on this paradigm and provide a reproducible comparison of their output on a classic learning task of pedestrian detection. Notably, the robustness of these approaches to corrupted data will be assessed. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.23919/Eusipco47968.2020.9287531 | 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020) |
Keywords | DocType | ISSN |
Supervised learning, Riemannian geometry, Covariance matrix, Pedestrian detection | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ammar Mian | 1 | 0 | 0.34 |
| Elias Raninen | 2 | 0 | 1.35 |
| Esa Ollila | 3 | 351 | 33.51 |