| Título |
A Survey of Vectorization Methods in Topological Data Analysis |
| Autores |
Ali, Dashti , Asaad, Aras , Jimenez, Maria-Jose , Nanda, Vidit , PALUZO HIDALGO, EDUARDO, Soriano-Trigueros, Manuel |
| Publicación externa |
Si |
| Medio |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
1 |
| Cuartil SJR |
1 |
| Impacto SJR |
6.158 |
| Ámbito |
Internacional |
| Fecha de publicacion |
01/12/2023 |
| ISI |
001104973300002 |
| DOI |
10.1109/TPAMI.2023.3308391 |
| Abstract |
Attempts to incorporate topological information in supervised learning tasks have resulted in the creation of several techniques for vectorizing persistent homology barcodes. In this paper, we study thirteen such methods. Besides describing an organizational framework for these methods, we comprehensively benchmark them against three well-known classification tasks. Surprisingly, we discover that the best-performing method is a simple vectorization, which consists only of a few elementary summary statistics. Finally, we provide a convenient web application which has been designed to facilitate exploration and experimentation with various vectorization methods. |
| Palabras clave |
Barcodes; persistent homology; topological data analysis; vectorization methods |
| Miembros de la Universidad Loyola |
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