Gaussian Process Latent Variable Alignment Learning

TL;DR

This paper introduces a probabilistic framework for unsupervised alignment of high-dimensional data sequences, capable of automatically inferring groupings and flexible warping with interpretable constraints.

Contribution

It presents a novel non-parametric probabilistic model that simultaneously learns alignments and data groupings, improving over existing methods.

Findings

Outperforms state-of-the-art alignment methods quantitatively

Automatically infers sequence groupings without supervision

Allows easy specification of high-level priors for different data modalities

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.