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Poster
Non-Stationary Spectral Kernels
Sami Remes · Markus Heinonen · Samuel Kaski

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #195
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We propose non-stationary spectral kernels for Gaussian process regression by modelling the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-stationary and non-monotonic kernels that can learn input-dependent and potentially long-range, non-monotonic covariances between inputs. We derive efficient inference using model whitening and marginalized posterior, and show with case studies that these kernels are necessary when modelling even rather simple time series, image or geospatial data with non-stationary characteristics.

Keywords: Gaussian Processes · Time Series Analysis · Signal Processing
[ Paper] 

Author Information

Sami Remes (Aalto University)
Markus Heinonen (Aalto University)
Samuel Kaski (Aalto University)

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