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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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Related topics

23 relations
C++Co-trainingDocument classificationDuality (optimization)

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
Two-Tier Precoding for FDD Multi-Cell Massive MIMO Time-Varying Interference Networks
Massive MIMO is a promising technology in future wireless communication networks. However, it raises a lot of implementation… 
Highly Cited
2013
Highly Cited
2013
Speaker adaptation of context dependent deep neural networks
There has been little work on examining how deep neural networks may be adapted to speakers for improved speech recognition… 
Highly Cited
2012
Highly Cited
2012
Model sparsity and brain pattern interpretation of classification models in neuroimaging
Highly Cited
2012
Highly Cited
2012
Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning
Iterative methods that calculate their steps from approximate subgradient directions have proved to be useful for stochastic… 
Highly Cited
2011
Highly Cited
2011
Fast global convergence of gradient methods for high-dimensional statistical recovery
Many statistical $M$-estimators are based on convex optimization problems formed by the combination of a data-dependent loss… 
Highly Cited
2011
Highly Cited
2011
Interactively building a discriminative vocabulary of nameable attributes
Human-name able visual attributes offer many advantages when used as mid-level features for object recognition, but existing… 
Highly Cited
2009
Highly Cited
2009
Hybrid Terminal Sliding-Mode Observer Design Method for a Permanent-Magnet Synchronous Motor Control System
This paper proposes a hybrid terminal sliding-mode observer based on the nonsingular terminal sliding-mode (NTSM) and the high… 
Highly Cited
2006
Highly Cited
2006
Manifold Denoising
We consider the problem of denoising a noisily sampled submanifold M in ℝd, where the submanifold M is a priori unknown and we… 
Highly Cited
2005
Highly Cited
2005
Limited feedback unitary precoding for orthogonal space-time block codes
Orthogonal space-time block codes (OSTBCs) are a class of easily decoded space-time codes that achieve full diversity order in… 
Highly Cited
2000
Highly Cited
2000
Relations Between Regularization and Diffusion Filtering
Regularization may be regarded as diffusion filtering with an implicit time discretization where one single step is used. Thus… 
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