Non-local manifold tangent learning. In: Proceedings of Advances in Neural Information Processing Systems. 2005, 17: 129–136Bengio, Y., & Monperrus, M. (2005). Non-local manifold tangent learning. In L. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing ...
In order to do that, we introduce two local manifold learning approaches: locally linear embedding and local tangent space alignment. The local geometric structure can be effectively modelled through these approaches. This framework is a general framework which also includes the NMF with Laplacian ...
The full rank covariance matrix, which lies on a Riemannian manifold, is projected on the tangent Euclidean space and concatenated to the mean vector for representing a given image. In this paper, we test the following features for describing the original image: scale-invariant feature transform ...
Another method firstly used Laplacian Eigenmaps to embed image patches in a lower-dimensional manifold that preserves local distances and then computed L2 distance of Laplacian images. However, the entropy image based method can only meet certain requirements of a relaxed version of the theoretical ...
The spacetime is thus a parallelizable manifold by the Weitzenböck connection. In this framework of teleparallelism, two distant vectors are considered parallel if they have the same local components relative to their preferred tetrad frames. Moreover, it follows from the tetrad orthonormality ...
Another method firstly used Laplacian Eigenmaps to embed image patches in a lower-dimensional manifold that preserves local distances and then computed L2 distance of Laplacian images. However, the entropy image based method can only meet certain requirements of a relaxed version of the theoretical ...