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Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
2004
Journal of Shanghai University (English Edition)
Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry of the manifold using tangent spaces learned by fitting an affine subspace in a neighborhood of each data point. Those
doi:10.1007/s11741-004-0051-1
fatcat:q5rhi7ikh5hnfd2zv3k3zhbyee