Relative unconstrained Least-Squares Importance Fitting (RuLSIF)IntroductionRuLSIF is an algorithm to directly estimate the relative denisty-ratio: \(r_{\alpha}({\mathbf x}) = \frac{p({\mathbf x})}{\alpha p({\mathbf x}) + (1 - \alpha)q({\mathbf x})}\) where \(0 \leq \alpha < 1\) is a parameter. In addition, using RuLSIF, the relative Pearson divergence (rPE) \(\mathrm{PE}_{\alpha}[p({\mathbf x}) || q({\mathbf x})] = \frac{1}{2}\int \left(\frac{p({\mathbf x})}{\alpha p({\mathbf x}) + (1 - \alpha)q({\mathbf x})} - 1\right)^2 (\alpha p({\mathbf x}) + (1 - \alpha)q({\mathbf x})) \mathrm{d}{\mathbf x}\) can be efficiently estimated. The Matlab code provides the function that computes the relative density-ratio and relative Pearson divergence (rPE). DownloadExamples (Toy data)Same distribution Different distribution AcknowledgementI am grateful to Prof. Masashi Sugiyama for his support in developing this software. ContactI am happy to have any kind of feedbacks. E-mail: yamada AT sg DOT cs DOT titech DOT ac DOT jp ReferenceYamada, M., Suzuki, T., Kanamori, T., Hachiya, H., & Sugiyama, M. |