Gaussian Process Latent Variable Alignment Learning
This addresses the problem of aligning complex data sequences without supervision, which is incremental but offers practical improvements.
The paper tackles unsupervised alignment learning between high-dimensional data by modeling both alignment and data simultaneously, achieving superior quantitative performance compared to state-of-the-art approaches.
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.