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| Content Provider | ACM Digital Library |
|---|---|
| Author | Dey, Tamal Krishna Fan, Fengtao Wang, Yusu |
| 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 Vietoris-Rips and witness complexes. While the ietoris-Rips complex is simple to compute and is a good vehicle for extracting topology of sampled spaces, its size is huge--particularly 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 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 Vietoris-Rips 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. |
| Starting Page | 107 |
| Ending Page | 116 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450320313 |
| DOI | 10.1145/2462356.2462387 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-06-17 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Graph induced complex Surface reconstruction Homology Topological data analysis |
| Content Type | Text |
| Resource Type | Article |
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