Unsupervised learning with contrastive latent variable models
This addresses the need for methods that work on sets of data rather than individual points or sequences, which is incremental as it builds on existing probabilistic models for unsupervised learning.
The authors tackled the problem of unsupervised dimensionality reduction for discovering patterns enriched in a target dataset relative to a background dataset, resulting in a probabilistic model that recovers interesting structure in the latent space and demonstrates applications in de-noising, feature selection, and subgroup discovery.
In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings.However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.