In this section
@inproceedings{Liu:2021:10.1101/2021.03.03.433709,
author = {Liu, Z and Barahona, M},
doi = {10.1101/2021.03.03.433709},
pages = {1332--1341},
publisher = {PMLR},
title = {Similarity measure for sparse time course data based on Gaussian processes},
url = {http://dx.doi.org/10.1101/2021.03.03.433709},
year = {2021}
}
TY - CPAPER
AB - 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.
AU - Liu,Z
AU - Barahona,M
DO - 10.1101/2021.03.03.433709
EP - 1341
PB - PMLR
PY - 2021///
SP - 1332
TI - Similarity measure for sparse time course data based on Gaussian processes
UR - http://dx.doi.org/10.1101/2021.03.03.433709
UR - https://proceedings.mlr.press/v161/liu21a.html
UR - http://hdl.handle.net/10044/1/93270
ER -