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multilinear gaussian process regression
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MLGP.jpg
MLGP.pdf
README.md
README.md~
RestaurantDataset.mat
grad_desc.m
mlgp_predict.m
mlgp_read_data.m
mlgp_train.m
test_mlgp.m
test_mlgp.m~

README.md

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.

Build Status Coverage Status Dependency Status

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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