zshwuhan / Polynomial_Kernel_Learning

GP on a set of real world data

Gaussian Processes on Graphs via Spectral Kernel Learning

This repository contains codes that produced the results in the paper Gaussian Processes on Graphs via Spectral Kernel Learning

We recommend starting with the gma folder which contains codes for learning on synthetic data.

main.py implements our proposed model of polynomial spectral kernel learning. The example command below computes a degree 2 polynomial on the weather data using 15 signals as training

python main.py --data=weather --training=15 --degree=2 --constrained=on

The real world datasets used in the paper are fmri, weather, and uber. For each dataset, the following training sizes are used in the paper:

• fmri: 21 and 42
• uber: 10 and 20
• weather: 15 and 30.

The --constrained parameter can be specified to off to skip constrained optimization.

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baselines.py computes the performances from the baseline models, a --model term needs to be specified instead of --degree and --constrained:

python baselines.py --data=weather --training=15 --model=standard

Baseline --model options are:

• standard
• laplacian
• local_averaging
• global_filtering
• regularized_laplacian
• diffusion
• 1_random_walk
• 3_random_walk
• cosine.