Constrained Least-Squares Density-Difference Estimation

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概要

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• We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing 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 incurred 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 on semi-supervised class-balance estimation under class-balance change.

• 一般社団法人電子情報通信学会の論文
• 2013-02-25

著者

• Sugiyama Masashi

  Tokyo Inst. Of Technol.

• KANAMORI Takafumi

  Nagoya University

• DU PLESSIS

  Tokyo Institute of Technology

• Nguyen Tuan

  Tokyo Institute of Technology

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