Contrastive Multivariate Singular Spectrum Analysis
This provides a domain-specific tool for time series analysis that shifts focus from variance to analyst-relevant signals, though it appears incremental as an extension of existing singular spectrum analysis.
The paper tackles the problem of unsupervised dimensionality reduction and signal decomposition for time series data by introducing Contrastive Multivariate Singular Spectrum Analysis, which identifies sub-signals enhanced in a target dataset relative to a background dataset rather than those with highest variance. The method is demonstrated on synthetic data and improves clustering of electrocardiogram signals from the MHEALTH dataset.
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.