Parameterless local discriminant embedding

In this paper, we propose a parameterless Local Discriminant Embedding. Recently, local discriminant embedding (LDE) method was proposed in order to tackle some limitations of the global linear discriminant analysis (LDA) method. LDE splits the graph Laplacian into two components: within-class adjacency graph and between-class adjacency graph to better characterize the discriminant property of the data. However, it is very difficult to set in advance the within- and between-class graphs. Our proposed LDE variant has two important characteristics: (i) while all graph-based manifold learning techniques (supervised and unsupervised) are depending on several parameters that require manual tuning, ours is parameter-free, and (ii) it adaptively estimates the local neighborhood surrounding each sample based on the data similarity. The resulting revisited LDE approach has been applied to the problem of model-less coarse 3D head pose estimation (person independent 3D pose estimation). It was tested on two large databases: FacePix and Pointing'04. It was conveniently compared with other linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones.

Parameterless local discriminant embedding Articles