Poisson Inverse Regression for sufficient dimension reduction in text data

被引：0

作者：

Gaba, Amarjit ^{[1
]}

Artemiou, Andreas ^{[2
]}

机构：

[1] Univ Oxford, Math Inst, Oxford, England

[2] Univ Limassol, Dept Informat Technol, Nicosia, Cyprus

来源：

STATISTICS AND ITS INTERFACE | 2025年 / 18卷 / 03期

关键词：

Dimension reduction; Exponential family; Poisson distribution; Multinomial inverse regression; Text data analysis;

D O I：

暂无

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In this work we present a sufficient dimension reduction (SDR) method for the analysis of text data. We use Poisson distribution to model word occurrences and therefore the method is called Poisson Inverse Regression. We provide the motivation for our example and adjust previously developed algorithms for sufficient dimension reduction for predictors from an exponential family distributions to develop our method. We demonstrate the validity of the proposed method using numerical experiments.

引用

页码：389 / 397

页数：9

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