Name
Abstract | ||
|---|---|---|
In a number of real-life applications, the user is interested in analyzing non vectorial data, for which kernels are useful tools that embed data into an (implicit) Euclidean space. However, when using such approaches with prototype-based methods, the computational time is related to the number of observations (because the prototypes are expressed as convex combinations of the original data). Also, a side effect of the method is that the interpretability of the prototypes is lost. In the present paper, we propose to overcome these two issues by using a bagging approach. The results are illustrated on simulated data sets and compared to alternatives found in the literature. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-07695-9_4 | ADVANCES IN SELF-ORGANIZING MAPS AND LEARNING VECTOR QUANTIZATION |
Field | DocType | Volume |
Kernel (linear algebra),Interpretability,Data mining,Data set,Euclidean space,Regular polygon,Artificial intelligence,Machine learning,Mathematics | Conference | 295 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérôme Mariette | 1 | 18 | 1.66 |
| Madalina Olteanu | 2 | 68 | 10.50 |
| Julien Boelaert | 3 | 2 | 0.73 |
| Nathalie Villa-Vialaneix | 4 | 72 | 10.94 |