On Coresets for Logistic Regression
Alexander Munteanu; Chris Schwiegelshohn; Christian Sohler; David P. Woodruff · 2018 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, 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, w
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Metadata source: arXiv