yuqirose / MultilinearGP

multilinear gaussian process regression

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Multi-linear Gaussian Process (MLGP)

High-order correlations are ubiquitous in modern data analytics. MLGP is a Gaussian process model that learns high-order structure in the data using multi-linear (tensor). kernel.

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Multilinear Gaussian process regression, implemented in MATLAB. See details in our AISTATS 2018 paper Tensor Regression meets Gaussian Processes

Test Example

example dataset

The Restaurant & Consumer Dataset contains data to build a restaurant recommender system where the objective is to predict consumer ratings given to different restaurants. Each of the p3 = 138 consumers gave p2 = 3 scores for food quality, service quality and overall quality. The dataset also contains p1 = 45 various descriptive attributes of the restaurants (such as geographical position, cuisine type and price band). We consider this to be a regression problem where the objective is to predict the scores given the attributes of a restaurant as an input query. Since there are 138 consumers, this leads to a multitask problem composed of 138x3 regression tasks

testing script

run test_mlgp.m

Citation

If you think the repo is useful, we kindly ask you to cite our work at

@article{yu2017tensor,
  title={Tensor Regression Meets Gaussian Processes},
  author={Yu, Rose and Li, Guangyu and Liu, Yan},
  booktitle={Artificial Intelligence and Statistics},
  year={2018}
}

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multilinear gaussian process regression


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Language:MATLAB 100.0%