Contrastive Multivariate Singular Spectrum Analysis
Machine Learning
2018-11-01 v1 Machine Learning
Abstract
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data. By utilizing an appropriate background dataset, the method transforms a target time series dataset in a way that evinces the sub-signals that are enhanced in the target dataset, as opposed to only those that account for the greatest variance. This shifts the goal from finding signals that explain the most variance to signals that matter the most to the analyst. We demonstrate our method on an illustrative synthetic example, as well as show the utility of our method in the downstream clustering of electrocardiogram signals from the public MHEALTH dataset.
Cite
@article{arxiv.1810.13317,
title = {Contrastive Multivariate Singular Spectrum Analysis},
author = {Abdi-Hakin Dirie and Abubakar Abid and James Zou},
journal= {arXiv preprint arXiv:1810.13317},
year = {2018}
}