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 language | English |
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| Publication status | Published - 9 Dec 2016 |
| Event | NIPS Workshop on Learning in High Dimensions with Structure - Duration: 9 Dec 2016 → … https://sites.google.com/site/structuredlearning16/ |
Workshop
| Workshop | NIPS Workshop on Learning in High Dimensions with Structure |
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| Period | 9/12/16 → … |
| 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, .