Efficient Metric Learning for the Analysis of Motion Data

Hosseini B, Hammer B (2015)
Presented at the Data Science and Advanced Analytics (DSAA), Paris, France.

Download
OA 350.02 KB
Konferenzbeitrag | Veröffentlicht | Englisch
Abstract / Bemerkung
We investigate metric learning in the context of dynamic time warping (DTW), the by far most popular dissimilarity measure used for the comparison and analysis of motion capture data. While metric learning enables a problem adapted representation of data, the majority of methods has been proposed for vectorial data only. In this contribution, we extend the popular principle offered by the large margin nearest neighbours learner (LMNN) to DTW by treating the resulting component-wise dissimilarity values as features. We demonstrate, that this principle greatly enhances the classification accuracy in several benchmarks. Further, we show that recent auxiliary concepts such as metric regularisation can be transferred from the vectorial case to component-wise DTW in a similar way. We illustrate, that metric regularisation constitutes a crucial prerequisite for the interpretation of the resulting relevance profiles.
Erscheinungsjahr
Konferenz
Data Science and Advanced Analytics (DSAA)
Konferenzort
Paris, France
Konferenzdatum
2015-10-19 – 2015-10-21
PUB-ID

Zitieren

Hosseini B, Hammer B. Efficient Metric Learning for the Analysis of Motion Data. Presented at the Data Science and Advanced Analytics (DSAA), Paris, France.
Hosseini, B., & Hammer, B. (2015). Efficient Metric Learning for the Analysis of Motion Data. Presented at the Data Science and Advanced Analytics (DSAA), Paris, France. doi:10.1109/DSAA.2015.7344819
Hosseini, B., and Hammer, B. (2015).“Efficient Metric Learning for the Analysis of Motion Data”. Presented at the Data Science and Advanced Analytics (DSAA), Paris, France.
Hosseini, B., & Hammer, B., 2015. Efficient Metric Learning for the Analysis of Motion Data. Presented at the Data Science and Advanced Analytics (DSAA), Paris, France.
B. Hosseini and B. Hammer, “Efficient Metric Learning for the Analysis of Motion Data”, Presented at the Data Science and Advanced Analytics (DSAA), Paris, France, 2015.
Hosseini, B., Hammer, B.: Efficient Metric Learning for the Analysis of Motion Data. Presented at the Data Science and Advanced Analytics (DSAA), Paris, France (2015).
Hosseini, Babak, and Hammer, Barbara. “Efficient Metric Learning for the Analysis of Motion Data”. Presented at the Data Science and Advanced Analytics (DSAA), Paris, France, 2015.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Volltext(e)
Name
Access Level
OA Open Access
Zuletzt Hochgeladen
2016-09-13T13:26:37Z

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Suchen in

Google Scholar
ISBN Suche