Learning sequence neighbourhood metrics

Authors:
Justin Bayer;Christian Osendorfer;Patrick van der Smagt
Affiliations:
Chair for Robotics and Embedded Systems, Insitut für Informatik, Technische Universität München, Germany;Chair for Robotics and Embedded Systems, Insitut für Informatik, Technische Universität München, Germany;Institute of Robotics and Mechatronics, DLR German Aerospace Center, Germany
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

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Abstract

Recurrent neural networks (RNNs) in combination with a pooling operator and the neighbourhood components analysis (NCA) objective function are able to detect the characterizing dynamics of sequences and embed them into a fixed-length vector space of arbitrary dimensionality. Subsequently, the resulting features are meaningful and can be used for visualization or nearest neighbour classification in linear time. This kind of metric learning for sequential data enables the use of algorithms tailored towards fixed length vector spaces such as ℝn.