【文件属性】：
文件名称：Metric Learning A Survey
文件大小：1.36MB
文件格式：PDF
更新时间：2022-01-28 19:21:07
机器学习 距离学习
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-neighbor methods and other
techniques that rely on distances or similarities. This survey presents
an overview of existing research in metric learning, including recent
progress on scaling to high-dimensional feature spaces and to data sets
with an extremely large number of data points. A goal of the survey is to
present as unified as possible a framework under which existing research
on metric learning can be cast. The first part of the survey focuses on
linear metric learning approaches, mainly concentrating on the class
of Mahalanobis distance learning methods. We then discuss nonlinear
metric learning approaches, focusing on the connections between the
nonlinear and linear approaches. Finally, we discuss extensions of metric
learning, as well as applications to a variety of problems in computer
vision, text analysis, program analysis, and multimedia.