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Manifold regularization

In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on… 
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C++Co-trainingDocument classificationDuality (optimization)

Broader (1)

Machine learning

Papers overview

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Highly Cited
2015
Highly Cited
2015
Low-rank matrix factorization with multiple Hypergraph regularizer
2013
2013
A Regularized MRI Image Reconstruction based on Hessian Penalty Term on CPU/GPU Systems
2012
2012
A Grassmann manifold-based domain adaptation approach
Domain adaptation algorithms that handle shifts in the distribution between training and testing data are receiving much… 
2010
2010
Generalized geometry in AdS/CFT and volume minimization
2010
2010
Hard modeling Multivariate Curve Resolution using LASSO: Application to Ion
2009
2009
Nonlinear regularization methods for ill-posed problems with piecewise constant or strongly varying solutions
In this paper we consider nonlinear ill-posed problems with piecewise constant or strongly varying solutions. A class of… 
2008
2008
Person-Independent Head Pose Estimation Using Biased Manifold Embedding
Head pose estimation has been an integral problem in the study of face recognition systems and human-computer interfaces, as part… 
2005
2005
Fast Reconstruction of Piecewise Smooth Signals from Incoherent Projections
The Compressed Sensing framework aims to recover a sparse signal from a small set of projections onto random vec tors; the… 
1997
1997
Extension of Isometries in Finite-Dimensional Indefinite Scalar Product Spaces and Polar Decompositions
Witt's theorem on the extension of H-isometries to H-unitary matrices with respect to the scalar product generated by a self… 
Highly Cited
1994
Highly Cited
1994
Stable methods for ill-posed variational problems : prox-regularization of elliptic variational inequalities and semi-infinite problems
Concepts of well-posedness stabilizing techniques for ill-posed problems Tikhonov's principle prox-regularization methods of… 
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