Uncoupled Nonnegative Matrix Factorization with Pairwise Comparison Data

被引：0

作者：

Kohjima, Masahiro ^{[1
]}

机构：

[1] NTT Corp, NTT Human Informat Labs, Yokosuka, Japan

来源：

PROCEEDINGS OF THE 2022 ACM SIGIR INTERNATIONAL CONFERENCE ON THE THEORY OF INFORMATION RETRIEVAL, ICTIR 2022 | 2022年

关键词：

uncoupled data; matrix factorization; Bregman divergence;

D O I：

10.1145/3539813.3545149

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a new method called uncoupled nonnegative matrix factorization (UNMF). UNMF enables us to analyze data that cannot be represented by a matrix, due to the lack of correspondence between the index and values of the matrix elements caused by e.g., data collection under the constraint of privacy protection. We derive the multiplicative update rules for parameter estimation and confirm the effectiveness of UNMF by numerical experiments.

引用

页码：2 / 6

页数：5

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