Year: 2015
Type: conference-paper
Abstract: 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.
Authors: Babak Hosseini, Barbara Hammer
Institution: Bielefeld University
Language: English
Topic: Human Motion and Animation
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1927681210, G3429453619
OpenAlex ID: W1849555551