Konferenzbeitrag
On Coresets for Logistic Regression
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Dokumententyp
Text/Conference Paper
Dateien
Datum
2019
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Quelle
INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft
Data Science
Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Coresets are one of the central methods to facilitate the analysis of large data.We continue a recent line of research applying the theory of coresets to logistic regression. First, we show the negative result that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable worst-case instances we introduce a complexity measure µ(X), which quantifies the hardness of compressing a data set for logistic regression. µ(X) has an intuitive statistical interpretation that may be of independent interest. For data sets with bounded µ(X)-complexity, we show that a novel sensitivity sampling scheme produces the first provably sublinear (1 ± ")-coreset.