Many algorithms rely critically on being given a good metric over their inputs. For instance,
data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K …

We address the problem of learning distance metrics using side-information in the form of
groups of" similar" points. We propose to use the RCA algorithm, which is a simple and …

The success of popular algorithms such as k-means clustering or nearest neighbor searches
depend on the assumption that the underlying distance functions reflect domain-specific …

Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the
distance metric for the input data patterns. Distance Metric learning is to learn a distance …

Finding an appropriate distance metric that accurately reflects the (dis) similarity between
examples is a key to the success of k-means clustering. While it is not always an easy task to …

Learning distance functions with side information plays a key role in many data mining
applications. Conventional distance metric learning approaches often assume that the target …

Distance metric has an important role in many machine learning algorithms. Recently, metric
learning for semi-supervised algorithms has received much attention. For semi-supervised …

Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …

The metric learning problem is concerned with learning a distance function tuned to a
particular task, and has been shown to be useful when used in conjunction with nearest …

A good distance metric is crucial for unsupervised learning from high-dimensional data. To
learn a metric without any constraint or class label information, most unsupervised metric …