Gaussian Process Latent Variable Alignment Learning

Ieva Kazlauskaite, Carl Henrik Ek

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner. Learning alignments is an ill-constrained problem as there are many different ways of defining a good alignment. Our proposed method casts alignment learning in a framework where both alignment and data are modelled simultaneously. 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 show results on several datasets, including different motion capture sequences and show that the suggested model outperform the classical algorithmic approaches to the alignment task.
Original languageEnglish
Title of host publicationThe 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan
EditorsKamalika Chaudhuri, Masashi Sugiyama
Number of pages10
Publication statusPublished - 1 Sept 2019

Publication series

NameProceedings of Machine Learning Research

Bibliographical note

11 pages, 9 figures


  • stat.ML
  • cs.LG


Dive into the research topics of 'Gaussian Process Latent Variable Alignment Learning'. Together they form a unique fingerprint.

Cite this