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.

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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title = "Learning Alignments from Latent Space Structures",
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",
day = "9",
language = "English",
note = "NIPS Workshop on Learning in High Dimensions with Structure ; Conference date: 09-12-2016",
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AU - Kazlauskaite, Ieva

AU - Ek, Carl Henrik

AU - Campbell, Neill

PY - 2016/12/9

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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.

M3 - Paper

ER -