Using Lasso RCCA for cross-language information retrieval

被引：1

作者：

Polajnar, Emil ^{[1
]}

机构：

[1] Univ Ljubljana, Fac Social Sci, Ljubljana, Slovenia

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2018年 / 47卷 / 09期

关键词：

Alternating least-squares; Cross-language information retrieval; Lasso shrinkage; Restricted canonical correlation; CCA;

D O I：

10.1080/03610918.2017.1359287

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Restricted canonical correlation analysis and the lasso shrinkage method were paired together for canonical correlation analysis with non-negativity restrictions on datasets, where a sample size is much smaller than the number of variables. The method was implemented in an alternating least-squares algorithm and applied to cross-language information retrieval on a dataset with aligned documents in eight languages. A set of experiments was ran to evaluate the method and compare it to other methods in the field.

引用

页码：2739 / 2748

页数：10

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