Similarity Measure for Sparse Time Course Data Based on Gaussian Processes
Abstract
We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP). The proposed GP similarity is similar to a Bayes factor and provides enhanced robustness to noise in sparse time series, such as those found in various biological settings, e.g., gene transcriptomics. We show that the GP measure is equivalent to the Euclidean distance when the noise variance in the GP is negligible compared to the noise variance of the signal. Our numerical experiments on both synthetic and real data show improved performance of the GP similarity when used in conjunction with two distance-based clustering methods.
Cite
Text
Liu and Barahona. "Similarity Measure for Sparse Time Course Data Based on Gaussian Processes." Uncertainty in Artificial Intelligence, 2021.Markdown
[Liu and Barahona. "Similarity Measure for Sparse Time Course Data Based on Gaussian Processes." Uncertainty in Artificial Intelligence, 2021.](https://mlanthology.org/uai/2021/liu2021uai-similarity/)BibTeX
@inproceedings{liu2021uai-similarity,
title = {{Similarity Measure for Sparse Time Course Data Based on Gaussian Processes}},
author = {Liu, Zijing and Barahona, Mauricio},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2021},
pages = {1332-1341},
volume = {161},
url = {https://mlanthology.org/uai/2021/liu2021uai-similarity/}
}