Scalable gaussian processes for characterizing multidimensional change surfaces

Scalable gaussian processes for characterizing multidimensional change surfaces

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to exibly define a change surface in combination with expressive spectral m...

Ausführliche Beschreibung

Bibliographische Detailangaben
Hauptverfasser: Herlands, W, Wilson, A, Nickisch, H, Flaxman, S, Neill, D, van Panhuis, W, Xing, E
Format: Conference item
Veröffentlicht: Journal of Machine Learning Research 2016
Beschreibung
Zusammenfassung:We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to exibly define a change surface in combination with expressive spectral mixture kernels to capture the complex statistical structure. Finally, through the use of novel methods for additive non-separable kernels, we can scale the model to large datasets. We demonstrate the model on numerical and real world data, including a large spatio-temporal disease dataset where we identify previously unknown heterogeneous changes in space and time.

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