Title | CCM: Controlling the Change Magnitude in High Dimensional Data |
Publication Type | Conference Proceedings |
Year of Conference | 2016 |
Authors | Alippi, C., G. Boracchi, and D. Carrera |
Conference Name | In Proceedings of the 2nd INNS Conference on Big Data 2016 (INNS Big Data 2016) |
Pagination | 1-10 |
Date Published | 10/2016 |
Conference Location | Thessaloniki, Greece |
Abstract | Change-detection algorithms are often tested on real-world datasets where changes are synthetically introduced. While this common practice allows generating multiple datasets to obtain stable performance measures, it is often quite arbitrary since the change magnitude is seldom controlled. Thus, experiments { in particular those on multivariate and high-dimensional data. We here present a rigorous framework for introducing changes having a controlled magnitude in multivariate datasets. In particular, we introduce changes by directly roto-translating the data, and we measure the change magnitude by the symmetric |
