Add to ORKGInternational audienceThe efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection between entities. The classic choice for this metric is usually a distance measure or a similarity measure based on the euclidean norm. We claim that in some cases the euclidean norm on the initial vectorial space might not be the more appropriate to solve the task efficiently. We propose an algorithm that aims at learning the most appropriate vectorial representation for building a graph on which the tas...
Many algorithms in pattern recognition and machine learning make use of some distance function expli...
Recent years have seen a growing number of graph-based semi-supervised learning methods. While the l...
In many applications non-metric distances are bet-ter than metric distances in reflecting the percep...
International audienceThe efficiency of graph-based semi-supervised algorithms depends on the graph ...
Inference Driven Metric Learning (IDML) for Graph Construction Graph-based semi-supervised learning ...
In many domain adaption formulations, it is assumed to have large amount of unlabeled data from the ...
We consider approximation algorithms for the metric labeling problem. Informally speaking, we are gi...
Distance Metric Learning (DML) seeks to learn a discriminative embedding where similar examples are ...
In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is co...
In the literature, most existing graph-based semi-supervised learning methods only use the label inf...
The need for appropriate ways to measure the distance or similarity between data is ubiquitous in ma...
Many machine learning and pattern recognition algorithms rely on a distance metric. Instead of choos...
Recent studies have shown that graph-based approaches are effective for semi-supervised learning. Th...
International audienceSimilarity between objects plays an important role in both human cognitive pro...
Many real-world networks are described by both connectivity information and features for every node....
Many algorithms in pattern recognition and machine learning make use of some distance function expli...
Recent years have seen a growing number of graph-based semi-supervised learning methods. While the l...
In many applications non-metric distances are bet-ter than metric distances in reflecting the percep...
International audienceThe efficiency of graph-based semi-supervised algorithms depends on the graph ...
Inference Driven Metric Learning (IDML) for Graph Construction Graph-based semi-supervised learning ...
In many domain adaption formulations, it is assumed to have large amount of unlabeled data from the ...
We consider approximation algorithms for the metric labeling problem. Informally speaking, we are gi...
Distance Metric Learning (DML) seeks to learn a discriminative embedding where similar examples are ...
In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is co...
In the literature, most existing graph-based semi-supervised learning methods only use the label inf...
The need for appropriate ways to measure the distance or similarity between data is ubiquitous in ma...
Many machine learning and pattern recognition algorithms rely on a distance metric. Instead of choos...
Recent studies have shown that graph-based approaches are effective for semi-supervised learning. Th...
International audienceSimilarity between objects plays an important role in both human cognitive pro...
Many real-world networks are described by both connectivity information and features for every node....
Many algorithms in pattern recognition and machine learning make use of some distance function expli...
Recent years have seen a growing number of graph-based semi-supervised learning methods. While the l...
In many applications non-metric distances are bet-ter than metric distances in reflecting the percep...