Three different localized representation methods and a manifold learning approach to face recognition are compared in terms of recognition accuracy.
In this paper, we propose a novel supervised manifold learning approach, supervised locality discriminant manifold learning (SLDML), for head pose estimation.
Other than recovering the whole 3D shapes and motion parameters like in almost all the existing applications, Rabaud and Belongie [16] presented a manifold learning approach that only focuses on an embedding of frames within the input image sequence.
In their paper, "A metric multidimensional scaling-based nonlinear manifold learning approach for unsupervised data reduction," C. Heinrich et al. propose a nonlinear extension to PCA for manifold learning that makes use of compression and regression along with a Bayesian projection procedure for out-of-sample extension.
This framework, although formulated for a regression scenario, unifies other supervised approaches to manifold learning that have been proposed so far.
Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace.
In summary, manifold learning methods, LPP or NFLE, discover the more intrinsic manifold structure than the global eigenspace methods, PCA and LDA.
This study proposes a novel multiple-domain manifold (MDM) method to achieve representative features based on singular value decomposition (SVD) and manifold learning.
The effectiveness and superiorities of the proposed method are demonstrated through several comparative experiments with other three manifold learning methods.
Moreover, for efficiently handling out-of-sample extension and learning with the local consistency, we decompose the manifold learning as a two-step approach.
