Name
Abstract | ||
|---|---|---|
Topological data analysis, such as persistent homology has shown beneficial properties for machine learning in many tasks. Topological representations, such as the persistence diagram (PD), however, have a complex structure (multiset of intervals) which makes it difficult to combine with typical machine learning workflows. We present novel compact fixed-size vectorial representations of PDs based on clustering and bag of words encodings that cope well with the inherent sparsity of PDs. Our novel representations outperform state-of-the-art approaches from topological data analysis and are computationally more efficient. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Bag-of-words model,Topological data analysis,Multiset,Theoretical computer science,Diagram,Persistent homology,Artificial intelligence,Cluster analysis,Workflow,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1802.04852 | 0 |
PageRank | References | Authors |
0.34 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bartosz Zielinski 0001 | 1 | 8 | 3.60 |
| Mateusz Juda | 2 | 12 | 2.74 |
| Matthias Zeppelzauer | 3 | 186 | 21.35 |