Sammanfattning
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.
Originalspråk | Engelska |
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Titel på värdpublikation | CD-MAKE 2017: Machine Learning and Knowledge Extraction |
Antal sidor | 11 |
Utgivningsort | Cham |
Förlag | Springer |
Utgivningsdatum | 24.08.2017 |
Sidor | 3-13 |
ISBN (tryckt) | 978-3-319-66807-9 |
ISBN (elektroniskt) | 978-3-319-66808-6 |
DOI | |
Status | Publicerad - 24.08.2017 |
MoE-publikationstyp | A4 Artikel i en konferenspublikation |
Evenemang | 2017 International Cross-Domain Conference for Machine Learning and Knowledge Extraction - Reggio, Italien Varaktighet: 29.08.2017 → 01.09.2017 |
Publikationsserier
Namn | Lecture Notes in Computer Science (LNCS) |
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Volym | 10410 |
Nyckelord
- 512 Företagsekonomi