%0 Journal Article
%T Visualizing Clusters in Artificial Neural Networks Using Morse Theory
%A Paul T. Pearson
%J Advances in Artificial Neural Systems
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/486363
%X This paper develops a process whereby a high-dimensional clustering problem is solved using a neural network and a low-dimensional cluster diagram of the results is produced using the Mapper method from topological data analysis. The low-dimensional cluster diagram makes the neural network's solution to the high-dimensional clustering problem easy to visualize, interpret, and understand. As a case study, a clustering problem from a diabetes study is solved using a neural network. The clusters in this neural network are visualized using the Mapper method during several stages of the iterative process used to construct the neural network. The neural network and Mapper clustering diagram results for the diabetes study are validated by comparison to principal component analysis. 1. Introduction Topological data analysis (TDA) is an emerging field of mathematics that focuses on constructing topological models for data and calculating algebraic invariants of such models [1¨C3]. The fundamental idea is to use methods from topology to determine shapes or patterns in high-dimensional data sets [4]. One method from TDA called Mapper constructs a low-dimensional topological model for a data set from the clusters in the level sets of a function on the data set [5]. This topological model for is a cluster diagram that shows the clusters in the level sets of (i.e., clusters in the layers of a stratification of ) and how clusters in adjacent, overlapping level sets are connected (i.e., how the neighboring layers are glued together). The topological model built in this way is analogous to how Morse theory is used to construct a cell decomposition of a manifold using sublevel sets of a Morse function on the manifold [5¨C7]. The resolution of the cluster diagram produced by Mapper can be adjusted by changing the level sets by varying the number, size, and shape of the regions used to cover the image of the function . Further, the Mapper method allows for different clustering algorithms to be used. The most important step for obtaining a useful topological model from Mapper is finding a function that solves a particular clustering problem of interest for a data set . This study examines the case when the function is a neural network. A feedforward, multilayer perceptron artificial neural network (hereafter called a neural network) is a function constructed by an iterative process in order to approximate a training function between two finite sets of points and called the inputs and target outputs, where and . In a context where a target output value represents the
%U http://www.hindawi.com/journals/aans/2013/486363/