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.

Publication
In 2020 28th European Signal Processing Conference (EUSIPCO)
Ammar Mian
Ammar Mian
Associate professor

Associate professor at Université Savoie Mont Blanc in Signal processing

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