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.
|Publication status||Published - 9 Dec 2016|
|Event||NIPS Workshop on Learning in High Dimensions with Structure - |
Duration: 9 Dec 2016 → …
|Workshop||NIPS Workshop on Learning in High Dimensions with Structure|
|Period||9/12/16 → …|