This article presents a selective survey of algorithms for the offline detection of multiple
change points in multivariate time series. A general yet structuring methodological strategy …

The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

In many financial applications, such as fraud detection, reject inference, and credit
evaluation, detecting clusters automatically is critical because it helps to understand the …

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 …

Motivated by recent successes on learning feature representations and on learning feature
comparison functions, we propose a unified approach to combining both for training a patch …

Recent innovations in training deep convolutional neural network (ConvNet) models have
motivated the design of new methods to automatically learn local image descriptors. The …

In this paper, we introduce two novel metric learning algorithms, χ2-LMNN and GB-LMNN,
which are explicitly designed to be non-linear and easy-to-use. The two approaches achieve …

Binary keypoint descriptors provide an efficient alternative to their floating-point competitors
as they enable faster processing while requiring less memory. In this paper, we propose a …

We are interested in the large-scale learning of Mahalanobis distances, with a particular
focus on person re-identification. We propose a metric learning formulation called Weighted …

Mean shift clustering is a powerful nonparametric technique that does not require prior
knowledge of the number of clusters and does not constrain the shape of the clusters …