Multiscale Methods and Machine Learning

Multiscale methods, in which a dataset is viewed and analyzed at different scales,are becoming more commonplace in machine learning recently and are proving to be valuable tools. At their core, multiscale methods capture the local geometry of neighborhoods defined by a series of distances between points or sets of nearest neighbors. This is a bit like viewing a part of a slide through a series of microscope resolutions. At high resolutions, very small features are captured in a small space within the sample. At lower resolutions, more of the slide is visible, and a person can investigate bigger features.Main advantages of multiscale methods include improved performance relative to state-of-the-art methods and dramatic reductions in necessary sample size to achieve these results.