Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold
This work addresses classification tasks for noisy signals, but it appears incremental as it combines existing techniques into a unified framework.
The paper tackled the problem of analyzing high-dimensional noisy signals by proposing a novel Riemannian joint dimensionality reduction and dictionary learning (R-JDRDL) method on symmetric positive definite manifolds for classification tasks, and demonstrated that it outperforms existing state-of-the-art algorithms in image classification.
Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools to analyze high-dimensional noisy signals. This paper presents a proposal of a novel Riemannian joint dimensionality reduction and dictionary learning (R-JDRDL) on symmetric positive definite (SPD) manifolds for classification tasks. The joint learning considers the interaction between dimensionality reduction and dictionary learning procedures by connecting them into a unified framework. We exploit a Riemannian optimization framework for solving DL and DR problems jointly. Finally, we demonstrate that the proposed R-JDRDL outperforms existing state-of-the-arts algorithms when used for image classification tasks.