Add to ORKGInternational audienceAs annotations of data can be scarce in large-scale practical problems, leveraging unlabelled examples is one of the most important aspects of machine learning. This is the aim of semi-supervised learning. To benefit from the access to unlabelled data, it is natural to diffuse smoothly knowledge of labelled data to unlabelled one. This induces to the use of Laplacian regularization. Yet, current implementations of Laplacian regularization suffer from several drawbacks, notably the well-known curse of dimensionality. In this paper, we provide a statistical analysis to overcome those issues, and unveil a large body of spectral filtering methods that exhibit desirable behaviors. They are implemented through (reproducing) ...
Abstract — Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labe...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
International audienceAs annotations of data can be scarce in large-scale practical problems, levera...
International audienceSemi-supervised Laplacian regularization, a standard graph-based approach for ...
International audienceWe study a semi-supervised learning method based on the similarity graph and R...
We discuss how a large class of regularization methods, collectively known as spectral regularizatio...
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing...
We study the behavior of the popular Laplacian Regularization method for Semi-Supervised Learning at...
Modern data-sets are often huge, possibly high-dimensional, and require complex non-linear parameter...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and man-ifold...
We discuss how a large class of regularization methods, collectively known as spectral regularizatio...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold ...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold ...
Supervised classification is one of the most powerful techniques to analyze data, when a-priori info...
Abstract — Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labe...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...
International audienceAs annotations of data can be scarce in large-scale practical problems, levera...
International audienceSemi-supervised Laplacian regularization, a standard graph-based approach for ...
International audienceWe study a semi-supervised learning method based on the similarity graph and R...
We discuss how a large class of regularization methods, collectively known as spectral regularizatio...
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing...
We study the behavior of the popular Laplacian Regularization method for Semi-Supervised Learning at...
Modern data-sets are often huge, possibly high-dimensional, and require complex non-linear parameter...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and man-ifold...
We discuss how a large class of regularization methods, collectively known as spectral regularizatio...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold ...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold ...
Supervised classification is one of the most powerful techniques to analyze data, when a-priori info...
Abstract — Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labe...
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold...
We are interested in using the goal of making predictions to influence dimensionality reduction proc...