Graph Induced Complex on Point Data
Abstract
The efficiency of extracting topological information from point data depends largely on the complex that is built on top of the data points. From a computational viewpoint, the most favored complexes for this purpose have so far been VietorisRips and witness complexes. While the VietorisRips complex is simple to compute and is a good vehicle for extracting topology of sampled spaces, its size is hugeparticularly in high dimensions. The witness complex on the other hand enjoys a smaller size because of a subsampling, but fails to capture the topology in high dimensions unless imposed with extra structures. We investigate a complex called the {\em graph induced complex} that, to some extent, enjoys the advantages of both. It works on a subsample but still retains the power of capturing the topology as the VietorisRips complex. It only needs a graph connecting the original sample points from which it builds a complex on the subsample thus taming the size considerably. We show that, using the graph induced complex one can (i) infer the one dimensional homology of a manifold from a very lean subsample, (ii) reconstruct a surface in three dimension from a sparse subsample without computing Delaunay triangulations, (iii) infer the persistent homology groups of compact sets from a sufficiently dense sample. We provide experimental evidences in support of our theory.
 Publication:

arXiv eprints
 Pub Date:
 April 2013
 arXiv:
 arXiv:1304.0662
 Bibcode:
 2013arXiv1304.0662D
 Keywords:

 Computer Science  Computational Geometry;
 Mathematics  Algebraic Topology
 EPrint:
 29th Annual Symposium on Computational Geometry, 2013 (to appear)