Visualization of Multivariate Density Estimates With Level Set Trees

Jussi Klemelä

Journal of Computational and Graphical Statistics · 2004 · 36 citations · 19 references

Concepts

TL;DR

The article introduces a method for visualizing multivariate functions. The method constructs a level set tree from separated level‑set components of a function, using various graphical representations to display mode counts, locations, excess masses, and shape features of multivariate density estimates. Simulations demonstrate that level set trees can uncover density modes that two‑dimensional projections miss, supporting their usefulness for exploratory data analysis.

Abstract

This article presents a method for visualization of multivariate functions. The method is based on a tree structure—called the level set tree—built from separated parts of level sets of a function. The method is applied for visualization of estimates of multivarate density functions. With different graphical representations of level set trees we may visualize the number and location of modes, excess masses associated with the modes, and certain shape characteristics of the estimate. Simulation examples are presented where projecting data to two dimensions does not help to reveal the modes of the density, but with the help of level set trees one may detect the modes. I argue that level set trees provide a useful method for exploratory data analysis.

References

19