ML Anthology

Non-Linear Matrix Factorization with Gaussian Processes

Neil D. Lawrence, Raquel Urtasun
ICML 2009 pp. 601-608
doi:10.1145/1553374.1553452 /icml/2009/lawrence2009icml-non/

Abstract

A popular approach to collaborative filtering is matrix factorization. In this paper we consider the "probabilistic matrix factorization" and by taking a latent variable model perspective we show its equivalence to Bayesian PCA. This inspires us to consider probabilistic PCA and its non-linear extension, the Gaussian process latent variable model (GP-LVM) as an approach for probabilistic non-linear matrix factorization. We apply approach to benchmark movie recommender data sets. The results show better than previous state-of-the-art performance.

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Text

Lawrence and Urtasun. "Non-Linear Matrix Factorization with Gaussian Processes." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553452

Markdown

[Lawrence and Urtasun. "Non-Linear Matrix Factorization with Gaussian Processes." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/lawrence2009icml-non/) doi:10.1145/1553374.1553452

BibTeX

@inproceedings{lawrence2009icml-non,
  title     = {{Non-Linear Matrix Factorization with Gaussian Processes}},
  author    = {Lawrence, Neil D. and Urtasun, Raquel},
  booktitle = {International Conference on Machine Learning},
  year      = {2009},
  pages     = {601-608},
  doi       = {10.1145/1553374.1553452},
  url       = {https://mlanthology.org/icml/2009/lawrence2009icml-non/}
}