To avoid such deficiencies, a manifold learning technique named maximum variance unfolding (MVU) is considered as an alternative.
In this paper, a framework is proposed for a supervised form of manifold learning called biased manifold embedding.
In this work, we propose a framework for a supervised form of manifold learning called Biased Manifold Embedding to obtain improved performance in head pose angle estimation.
We incorporate the manifold learning and the regression with a learned discriminant manifold-based projection function obtained by discriminatively Laplacian regularized least squares.
Second, the proposed manifold learning method, NFLE, preserves the local structures among samples in manifold distributions.
Figure 5 Constructing contour trees using manifold learning techniques [[16]].
Manifold learning has become a hot issue in the field of machine learning and data mining.
Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace.
Nonlinear dimensionality reduction of data lying on multi-cluster manifolds is a crucial issue in manifold learning research.
In this paper, we propose a novel supervised manifold learning approach, supervised locality discriminant manifold learning (SLDML), for head pose estimation.
