Bayesian inverse regression for dimension reduction with small datasets
This work addresses dimension reduction challenges in supervised learning, particularly for small datasets, offering a method that avoids data slicing and provides a Bayesian alternative to existing techniques.
The paper tackles the problem of supervised dimension reduction for small datasets by proposing a Bayesian framework that computes the conditional distribution of predictors given the response using Gaussian process regression and Monte Carlo sampling, demonstrating effectiveness in small data scenarios.
We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors $\-x$ which can retain the statistical relationship between $\-x$ and the response variable $y$. We follow the idea of the sliced inverse regression (SIR) and the sliced average variance estimation (SAVE) type of methods, which is to use the statistical information of the conditional distribution $π(\-x|y)$ to identify the dimension reduction (DR) space. In particular we focus on the task of computing this conditional distribution without slicing the data. We propose a Bayesian framework to compute the conditional distribution where the likelihood function is obtained using the Gaussian process regression model. The conditional distribution $π(\-x|y)$ can then be computed directly via Monte Carlo sampling. We then can perform DR by considering certain moment functions (e.g. the first or the second moment) of the samples of the posterior distribution. With numerical examples, we demonstrate that the proposed method is especially effective for small data problems.