Speaker
Description
A major problem for time series clustering is that computing the similarity matrix for the most used similarity measures becomes infeasible if number amount or length of time series becomes too large. However, since this similarity matrix typically has low-rank structure, it can be approximated using a low-rank approximation. In this work, we show that existing numerical linear algebra methods, more specifically Randomly Pivoted Cholesky can be used in the context of time series clustering to drastically reduce the computational cost of calculating the similarity matrix, while maintaining the clustering quality. This shows that low-rank approximation algorithms are an effective and scalable technique that can be used in time series clustering.