Learning Alignments from Latent Space Structures

Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell

Research output: Contribution to conferencePaperpeer-review

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In this paper we present a model that is capable of learning alignments between high-dimensional data by exploiting low-dimensional structures. Specifically, our method uses a Gaussian process latent variable model (GP-LVM) to learn alignments and latent representations simultaneously. The results show that our model performs alignment implicitly and improves the smoothness of the low dimensional representations.
Original languageEnglish
Publication statusPublished - 9 Dec 2016
EventNIPS Workshop on Learning in High Dimensions with Structure -
Duration: 9 Dec 2016 → …


WorkshopNIPS Workshop on Learning in High Dimensions with Structure
Period9/12/16 → …
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