A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
This addresses a practical problem for domains with irregular time series data, offering a general framework, though it appears incremental as it builds on existing methods like structured kernel interpolation.
The authors tackled classification of sparse, irregularly-sampled time series by proposing a Gaussian process adapter layer that connects such data to any gradient-descent-learnable classifier, enabling uncertainty-aware, end-to-end training with scalable computations.
We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume fixed-dimensional feature spaces. To address these challenges, we propose an uncertainty-aware classification framework based on a special computational layer we refer to as the Gaussian process adapter that can connect irregularly sampled time series data to any black-box classifier learnable using gradient descent. We show how to scale up the required computations based on combining the structured kernel interpolation framework and the Lanczos approximation method, and how to discriminatively train the Gaussian process adapter in combination with a number of classifiers end-to-end using backpropagation.