Gaussian Process Latent Variable Alignment Learning

Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:748-757, 2019.

Abstract

We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner. Our proposed method casts alignment learning in a framework where both alignment and data are modelled simultaneously. Further, we automatically infer groupings of different types of sequences within the same dataset. We derive a probabilistic model built on non-parametric priors that allows for flexible warps while at the same time providing means to specify interpretable constraints. We demonstrate the efficacy of our approach with superior quantitative performance to the state-of-the-art approaches and provide examples to illustrate the versatility of our model in automatic inference of sequence groupings, absent from previous approaches, as well as easy specification of high level priors for different modalities of data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-kazlauskaite19a, title = {Gaussian Process Latent Variable Alignment Learning}, author = {Kazlauskaite, Ieva and Ek, Carl Henrik and Campbell, Neill}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {748--757}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/kazlauskaite19a/kazlauskaite19a.pdf}, url = {https://proceedings.mlr.press/v89/kazlauskaite19a.html}, abstract = {We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner. Our proposed method casts alignment learning in a framework where both alignment and data are modelled simultaneously. Further, we automatically infer groupings of different types of sequences within the same dataset. We derive a probabilistic model built on non-parametric priors that allows for flexible warps while at the same time providing means to specify interpretable constraints. We demonstrate the efficacy of our approach with superior quantitative performance to the state-of-the-art approaches and provide examples to illustrate the versatility of our model in automatic inference of sequence groupings, absent from previous approaches, as well as easy specification of high level priors for different modalities of data.} }
Endnote
%0 Conference Paper %T Gaussian Process Latent Variable Alignment Learning %A Ieva Kazlauskaite %A Carl Henrik Ek %A Neill Campbell %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-kazlauskaite19a %I PMLR %P 748--757 %U https://proceedings.mlr.press/v89/kazlauskaite19a.html %V 89 %X We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner. Our proposed method casts alignment learning in a framework where both alignment and data are modelled simultaneously. Further, we automatically infer groupings of different types of sequences within the same dataset. We derive a probabilistic model built on non-parametric priors that allows for flexible warps while at the same time providing means to specify interpretable constraints. We demonstrate the efficacy of our approach with superior quantitative performance to the state-of-the-art approaches and provide examples to illustrate the versatility of our model in automatic inference of sequence groupings, absent from previous approaches, as well as easy specification of high level priors for different modalities of data.
APA
Kazlauskaite, I., Ek, C.H. & Campbell, N.. (2019). Gaussian Process Latent Variable Alignment Learning. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:748-757 Available from https://proceedings.mlr.press/v89/kazlauskaite19a.html.

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