Riemannian Manifold Optimization for Discriminant Subspace Learning

TL;DR

This paper introduces Riemannian-based discriminant analysis (RDA), a novel approach transforming traditional LDA into a Riemannian manifold optimization problem, leading to improved classification accuracy.

Contribution

The paper proposes a new Riemannian manifold optimization method for discriminant subspace learning, overcoming local minima issues in Euclidean-based LDA.

Findings

RDA outperforms existing LDA variants in classification accuracy.

RDA achieves state-of-the-art performance on image classification tasks.

The trust-region method effectively learns discriminant subspaces on Riemannian manifolds.

Abstract

Linear discriminant analysis (LDA) is a widely used algorithm in machine learning to extract a low-dimensional representation of high-dimensional data, it features to find the orthogonal discriminant projection subspace by using the Fisher discriminant criterion. However, the traditional Euclidean-based methods for solving LDA are easily convergent to spurious local minima and hardly obtain an optimal solution. To address such a problem, in this paper, we propose a novel algorithm namely Riemannian-based discriminant analysis (RDA) for subspace learning. In order to obtain an explicit solution, we transform the traditional Euclidean-based methods to the Riemannian manifold space and use the trust-region method to learn the discriminant projection subspace. We compare the proposed algorithm to existing variants of LDA, as well as the unsupervised tensor decomposition methods on image classification tasks. The numerical results suggest that RDA achieves state-of-the-art performance in classification accuracy.