- 著者
- Tuan Duong NGUYEN Marthinus Christoffel DU PLESSIS Takafumi KANAMORI Masashi SUGIYAMA
- 出版者
- The Institute of Electronics, Information and Communication Engineers
- 雑誌
- IEICE Transactions on Information and Systems (ISSN:09168532)
- 巻号頁・発行日
- vol.E97.D, no.7, pp.1822-1829, 2014 (Released:2014-07-01)
- 参考文献数
- 22
- 被引用文献数
- 2 5
We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure that first estimates two densities separately and then computes their difference. However, such a two-step procedure does not necessarily work well because the first step is performed without regard to the second step and thus a small error in the first stage can cause a big error in the second stage. Recently, a single-shot method called the least-squares density-difference (LSDD) estimator has been proposed. LSDD directly estimates the density difference without separately estimating two densities, and it was demonstrated to outperform the two-step approach. In this paper, we propose a variation of LSDD called the constrained least-squares density-difference (CLSDD) estimator, and theoretically prove that CLSDD improves the accuracy of density difference estimation for correctly specified parametric models. The usefulness of the proposed method is also demonstrated experimentally.