Name
Abstract | ||
|---|---|---|
Most Machine Learning techniques traditionally rely on some forms of Euclidean Distances, computed in a Euclidean space (typically R-d). In more general cases, data might not live in a classical Euclidean space, and it can be difficult (or impossible) to find a direct representation for it in R-d. Therefore, distance mapping from a non-Euclidean space to a canonical Euclidean space is essentially needed. We present in this paper a possible distance-mapping algorithm, such that the behavior of the pairwise distances in the mapped Euclidean space is preserved, compared to those in the original non-Euclidean space. Experimental results of the mapping algorithm are discussed on a specific type of datasets made of timestamped GPS coordinates. The comparison of the original and mapped distances, as well as the standard errors of the mapped distributions, are discussed. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-66808-6_1 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 10410 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Ren | 1 | 214 | 21.37 |
| Yoan Miche | 2 | 1054 | 54.56 |
| Ian Oliver | 3 | 0 | 0.68 |
| Silke Holtmanns | 4 | 34 | 10.66 |
| Kaj-Mikael Björk | 5 | 148 | 16.40 |
| Amaury Lendasse | 6 | 1876 | 126.03 |