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JMLR
2006
186views more  JMLR 2006»
13 years 7 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
12 years 11 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
14 years 2 months 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»
13 years 7 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
12 years 11 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