Using unsupervised learning to improve prediction performance

The TDA models have by far the richest functionality and are, unsurprisingly, what we use in our work. They include all the capabilities described above. TDA begins with a similarity measure on a data set X, and then constructs a graph for X which acts as a similarity map or similarity model for it. Each node in the graph corresponds to a sub-collection of X. Pairs of points which lie in the same node or in adjacent nodes are more similar to each other than pairs which lie in nodes far removed from each other in the graph structure. The graphical model can of course be visualized, but it has a great deal of other functionality.