Data Association with Gaussian Processes
This work addresses the data association problem for scenarios with multiple data sources, noise, or multimodality, presenting an incremental improvement through a Bayesian framework.
The authors tackled the data association problem, which involves separating data from different generating processes, by introducing a fully Bayesian approach using Gaussian process priors, achieving simultaneous solution of data association and supervised learning with an efficient learning scheme based on doubly stochastic variational inference.
The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significant noise, or exhibit multimodality. We present a fully Bayesian approach to this problem. Our model is capable of simultaneously solving the data association problem and the induced supervised learning problems. Underpinning our approach is the use of Gaussian process priors to encode the structure of both the data and the data associations. We present an efficient learning scheme based on doubly stochastic variational inference and discuss how it can be applied to deep Gaussian process priors.