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… (More)
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Topic mentions per year

Topic mentions per year

2003-2018
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Papers overview

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2015
2015
Nonnegative Matrix Factorization (NMF) has been extensively applied in many areas, including computer vision, pattern recognition… (More)
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2013
2013
This paper proposes a new model of low-rank matrix factorization that incorporates manifold regularization to the matrix… (More)
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2012
2012
Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled… (More)
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Highly Cited
2011
Highly Cited
2011
Nonnegative matrix factorization (NMF) has become a popular data-representation method and has been widely used in image… (More)
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2010
2010
We present a novel method for multitask learning (MTL) based on manifold regularization: assume that all task parameters lie on a… (More)
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Highly Cited
2009
Highly Cited
2009
We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems… (More)
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2009
2009
Feature selection plays a fundamental role in many pattern recognition problems. However, most efforts have been focused on the… (More)
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Highly Cited
2009
Highly Cited
2009
Feature selection has attracted a huge amount of interest in both research and application communities of data mining. We… (More)
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Highly Cited
2008
Highly Cited
2008
Inspired by co-training, many multi-view semi-supervised kernel methods implement the following idea: find a function in each of… (More)
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Highly Cited
2006
Highly Cited
2006
Semi-supervised learning is more powerful than supervised learning by using both labeled and unlabeled data. In particular, the… (More)
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