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JMLR
2006
186views more  JMLR 2006»
15 years 6 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
COMPGEOM
2011
ACM
14 years 9 months ago
Persistence-based clustering in riemannian manifolds
We present a clustering scheme that combines a mode-seeking phase with a cluster merging phase in the corresponding density map. While mode detection is done by a standard graph-b...
Frédéric Chazal, Leonidas J. Guibas,...
IDA
2009
Springer
16 years 23 days ago
Soft Dimension Reduction for ICA by Joint Diagonalization on the Stiefel Manifold
Abstract. Joint diagonalization for ICA is often performed on the orthogonal group after a pre-whitening step. Here we assume that we only want to extract a few sources after pre-w...
Fabian J. Theis, Thomas P. Cason, Pierre-Antoine A...
IVC
2007
184views more  IVC 2007»
15 years 6 months ago
Image distance functions for manifold learning
Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of bas...
Richard Souvenir, Robert Pless
ISBI
2011
IEEE
14 years 10 months ago
Laplace-Beltrami eigenfunction expansion of cortical manifolds
We represent a shape representation technique using the eigenfunctions of Laplace-Beltrami operator and compare the performance with the conventional spherical harmonic (SPHARM) r...
Seongho Seo, Moo K. Chung