Topological data analysis is a new area of study aimed at having applications in areas such as data mining and computer vision. The main problems are:

1. how one infers high-dimensional structure from low-dimensional representations; and
2. how one assembles discrete points into global structure.

The human brain can easily extract global structure from representations in a strictly lower dimension, i.e. we infer a 3D environment from a 2D image from each eye. The inference of global structure also occurs when converting discrete data into continuous images, e.g. dot-matrix printers and televisions communicate images via arrays of discrete points.

The main method used by topological data analysis is:

1. Replace a set of data points with a family of simplicial complexes, indexed by a proximity parameter.
2. Analyse these topological complexes via algebraic topology — specifically, via the theory of persistent homology.^[1]
3. Encode the persistent homology of a data set in the form of a parameterized version of a Betti number which is called a barcode.^[2]