Name
Title | ||
|---|---|---|
Computing persistent features in big data: A distributed dimension reduction approach |
Abstract | ||
|---|---|---|
Persistent homology has become one of the most popular tools used in topological data analysis for analyzing big data sets. In an effort to minimize the computational complexity of finding the persistent homology of a data set, we develop a simplicial collapse algorithm called the selective collapse. This algorithm works by representing the previously developed strong collapse as a forest and uses that forest data to improve the speed of both the strong collapse and of persistent homology. Finally, we demonstrate the savings in computational complexity using geometric random graphs. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6853548 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
computational complexity,data analysis,data reduction,graph theory,computational complexity,distributed dimension reduction,geometric random graphs,persistent homology,selective collapse,simplicial collapse algorithm,topological data analysis,Simplicial complex,persistent homology,simplicial collapse,strong collapse,topological data analysis | Graph theory,Topological data analysis,Topology,Mathematical optimization,Algorithm design,Random graph,Computer science,Theoretical computer science,Persistent homology,Simplicial complex,Big data,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
1520-6149 | 4 | 0.42 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam C. Wilkerson | 1 | 4 | 0.42 |
| Harish Chintakunta | 2 | 36 | 6.05 |
| Hamid Krim | 3 | 520 | 59.69 |