# Bayesian inverse regression for dimension reduction with small datasets

## TL;DR

This paper introduces a Bayesian inverse regression method using Gaussian process models to perform supervised dimension reduction, especially effective with small datasets, by directly estimating the conditional distribution without data slicing.

## Contribution

The paper proposes a novel Bayesian framework for dimension reduction that avoids data slicing and utilizes Gaussian process regression to estimate the conditional distribution.

## Key findings

- Effective for small data problems
- Performs dimension reduction without data slicing
- Demonstrates promising numerical results

## Abstract

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 $\pi(\-x|y)$ 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 $\pi(\-x|y)$ 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.