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Found 3 papers, 1 papers with code
Minimal Sample Subspace Learning: Theory and Algorithms
Subspace segmentation or subspace learning is a challenging and complicated task in machine learning.
Principal Boundary on Riemannian Manifolds
We consider the classification problem and focus on nonlinear methods for classification on manifolds.
Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment
Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications.
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