CLASSIFICATION OF HIGH DIMENSIONAL DATA

摘要

A method for classification of high dimensional data on graphs based on the Ginzburg-Landau functional. The method applies L2 gradient flow minimization of the Ginzburg-Landau diffuse interface energy functional to the case of functions defined on graphs. The method performs binary segmentations in a semi-supervised learning (SSL) framework and multiclass tasks are solved by recursively applying a sequence of binary segmentations. Examples illustrate the versatility of the methods on a variety of datasets including congressional voting records, high dimensional test data, and machine learning in image processing.

主权项

1. A method for classifying high dimensional data comprising:
(a) specifying an initial set of features within a data set of high dimensional data; (b) determining edge weights with a similarity function w(x,y); (c) building a graph based on the determined edge weights; (d) minimizing a Ginzburg-Landau energy functional with one or more constraints or fidelity terms; and (e) segmenting data into two classes.