Generalized Prediction Intervals for Arbitrary Distributed High-Dimensional Data

TL;DR

This paper introduces a novel method to generalize prediction intervals for high-dimensional data using significance level distributions, facilitating outlier detection and one-class classification.

Contribution

It proposes a new approach that transforms probability density functions into significance level distributions, enabling interval-independent probability assessments in high-dimensional spaces.

Findings

Enables direct outlier detection in high-dimensional data

Provides a generalized framework for prediction intervals

Facilitates one-class classification tasks

Abstract

This paper generalizes the traditional statistical concept of prediction intervals for arbitrary probability density functions in high-dimensional feature spaces by introducing significance level distributions, which provides interval-independent probabilities for continuous random variables. The advantage of the transformation of a probability density function into a significance level distribution is that it enables one-class classification or outlier detection in a direct manner.