Projects per year
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.
| Original language | English |
|---|---|
| Title of host publication | 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings |
| Publisher | EURASIP – European Association For Signal Processing |
| Pages | 950-954 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789082797053 |
| DOIs | |
| Publication status | Published - 2020 |
| MoE publication type | A4 Article in a conference publication |
| Event | European Signal Processing Conference - Amsterdam, Netherlands Duration: 24 Aug 2020 → 28 Aug 2020 Conference number: 28 |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| ISSN (Print) | 2219-5491 |
| ISSN (Electronic) | 2076-1465 |
Conference
| Conference | European Signal Processing Conference |
|---|---|
| Abbreviated title | EUSIPCO |
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 24/08/2020 → 28/08/2020 |
Keywords
- Covariance matrix
- Pedestrian detection
- Riemannian geometry
- Supervised learning
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Dive into the research topics of 'A comparative study of supervised learning algorithms for symmetric positive definite features'. Together they form a unique fingerprint.Projects
- 1 Finished
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Robust Statistics for High-dimensional Data
Ollila, E., Raninen, E., Basiri, S., Tabassum, M. N. & Mian, A.
01/09/2016 → 31/12/2020
Project: Academy of Finland: Other research funding