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Learning Sequence Neighbourhood Metrics

Bayer, Justin and Osendorfer, Christian and van der Smagt, Patrick (2011) Learning Sequence Neighbourhood Metrics. NIPS 2011, Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity , TUM.

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Storing short descriptors of sequential data has several benefits. First, they typically require much less memory and thus make processing of large data sets much more efficient. Second, if the descriptors are formed as vectors, e.g. x 2 Rn, numerous algorithms tailored towards static data can be applied. Instead of applying static data algorithms to dynamic data, we propose to learn a mapping from sequential data to static data first. This can be done by combining recurrent neural networks (RNNs), a pooling operation and any differentiable objective function for static data. In this work, we present how neigbourhood components analysis (NCA) (Goldberger et al. 2004) can be used to learn meaningful representations which lead to excellent classification results and visualizations on a speech dataset.

Document Type:Conference or Workshop Item (Speech, Paper)
Title:Learning Sequence Neighbourhood Metrics
AuthorsInstitution or Email of Authors
Bayer, Justin Technische Universität München
Osendorfer, Christian Technische Universität München
van der Smagt, Patrick smagt@dlr.de
Refereed publication:No
In ISI Web of Science:No
Keywords:Learning Sequence
Event Title:NIPS 2011, Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity
Event Location:TUM
Event Type:Workshop
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W SY - Technik für Raumfahrtsysteme
DLR - Research area:Space
DLR - Program:W SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):W - RMC - Kognitive Intelligenz und Autonomie (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics > Robotic Systems
Deposited By: Gabriele Beinhofer
Deposited On:20 Jan 2012 11:34
Last Modified:20 Jan 2012 11:34

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