We incorporate the manifold learning and the regression with a learned discriminant manifold-based projection function obtained by discriminatively Laplacian regularized least squares.
Guided by the manifold learning embedding results, a novel cache size optimization scheme is developed.
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.
In this paper, a framework is proposed for a supervised form of manifold learning called biased manifold embedding.
In summary, manifold learning methods, LPP or NFLE, discover the more intrinsic manifold structure than the global eigenspace methods, PCA and LDA.
