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- Title
PCA-based drift and shift quantification framework for multidimensional data.
- Authors
Goldenberg, Igor; Webb, Geoffrey I.
- Abstract
Concept drift is a serious problem confronting machine learning systems in a dynamic and ever-changing world. In order to manage concept drift it may be useful to first quantify it by measuring the distance between distributions that generate data before and after a drift. There is a paucity of methods to do so in the case of multidimensional numeric data. This paper provides an in-depth analysis of the PCA-based change detection approach, identifies shortcomings of existing methods and shows how this approach can be used to measure a drift, not merely detect it.
- Subjects
DYNAMICAL systems; CONCEPT mapping
- Publication
Knowledge & Information Systems, 2020, Vol 62, Issue 7, p2835
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-020-01438-3