Add to ORKGProceedings of the 26th International Conference On Machine Learning, ICML 2009841-84
textA large number of machine learning algorithms are critically dependent on the underlying distanc...
Recently, many machine learning problems rely on a valuable tool: metric learning. However, in many ...
Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 200
In this paper we study the problem of learning a low-rank (sparse) distance ma-trix. We propose a no...
In plenty of scenarios, data can be represented as vectors and then mathematically abstracted as poi...
Choosing a distance preserving measure or metric is fun-damental to many signal processing algorithm...
Metric learning has become a widespreadly used tool in machine learning. To reduce expensive costs ...
International audienceWe propose a new approach for metric learning by framing it as learning a spar...
A good distance metric can improve the accuracy of a nearest neighbour classifier. Xing et al. (200...
Brinkrolf J, Hammer B. Sparse Metric Learning in Prototype-based Classification. In: Verleysen M, ed...
Although distance metric learning has been successfully applied to many real-world applications, lea...
Schulz A, Hammer B. Metric Learning in Dimensionality Reduction. In: Proceedings of the Internation...
The central problem for most existing metric learning methods is to find a suitable projection matri...
Abstract. We study data-adaptive dimensionality reduction in the context of supervised learning in g...
Various regularization techniques are investigated in supervised learning from data. Theoretical fea...
textA large number of machine learning algorithms are critically dependent on the underlying distanc...
Recently, many machine learning problems rely on a valuable tool: metric learning. However, in many ...
Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 200
In this paper we study the problem of learning a low-rank (sparse) distance ma-trix. We propose a no...
In plenty of scenarios, data can be represented as vectors and then mathematically abstracted as poi...
Choosing a distance preserving measure or metric is fun-damental to many signal processing algorithm...
Metric learning has become a widespreadly used tool in machine learning. To reduce expensive costs ...
International audienceWe propose a new approach for metric learning by framing it as learning a spar...
A good distance metric can improve the accuracy of a nearest neighbour classifier. Xing et al. (200...
Brinkrolf J, Hammer B. Sparse Metric Learning in Prototype-based Classification. In: Verleysen M, ed...
Although distance metric learning has been successfully applied to many real-world applications, lea...
Schulz A, Hammer B. Metric Learning in Dimensionality Reduction. In: Proceedings of the Internation...
The central problem for most existing metric learning methods is to find a suitable projection matri...
Abstract. We study data-adaptive dimensionality reduction in the context of supervised learning in g...
Various regularization techniques are investigated in supervised learning from data. Theoretical fea...
textA large number of machine learning algorithms are critically dependent on the underlying distanc...
Recently, many machine learning problems rely on a valuable tool: metric learning. However, in many ...
Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 200