Abstract

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 → …
https://sites.google.com/site/structuredlearning16/

Workshop

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

Cite this

Kazlauskaite, I., Ek, C. H., & Campbell, N. (2016). Learning Alignments from Latent Space Structures. Paper presented at NIPS Workshop on Learning in High Dimensions with Structure, .

Learning Alignments from Latent Space Structures. / Kazlauskaite, Ieva; Ek, Carl Henrik; Campbell, Neill.

2016. Paper presented at NIPS Workshop on Learning in High Dimensions with Structure, .

Research output: Contribution to conferencePaper

Kazlauskaite, I, Ek, CH & Campbell, N 2016, 'Learning Alignments from Latent Space Structures' Paper presented at NIPS Workshop on Learning in High Dimensions with Structure, 9/12/16, .
Kazlauskaite I, Ek CH, Campbell N. Learning Alignments from Latent Space Structures. 2016. Paper presented at NIPS Workshop on Learning in High Dimensions with Structure, .
Kazlauskaite, Ieva ; Ek, Carl Henrik ; Campbell, Neill. / Learning Alignments from Latent Space Structures. Paper presented at NIPS Workshop on Learning in High Dimensions with Structure, .
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abstract = "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.",
author = "Ieva Kazlauskaite and Ek, {Carl Henrik} and Neill Campbell",
year = "2016",
month = "12",
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note = "NIPS Workshop on Learning in High Dimensions with Structure ; Conference date: 09-12-2016",
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AU - Kazlauskaite, Ieva

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N2 - 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.

AB - 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.

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