Golden Recursion Inc. logoGolden Recursion Inc. logo
Advanced Search
Archetypal analysis

Archetypal analysis

Methodology in statistics and unsupervised learning that represents each "individual" in a data set as a mixture of "individuals of pure type", or "archetypes."

Archetypal analysis (AA) is a methodology in statistics and unsupervised learning that represents each "individual" in a data set as a mixture of "individuals of pure type", or "archetypes." Computing the archetypes is a nonlinear least squares problem which is solved using an alternative minimizing algorithm.

Archetypal analysis was originally proposed by Adele Cutler and Leo Breiman as an alternative to principal component analysis (PCA) for discovering latent factors for high-dimensional data. AA estimates the principal convex hull of a data set, and each "archetype" (i.e. factor) is forced to be a convex combination of extremal points of the data. The associations between archetypes and data points contributes to AA's results being easily interpretable.

The archetypal analysis methodology allows for dimensionality reduction and clustering. The disadvantage of AA is that its computation costs increase quadratically with the number of data points in a set, making it impractical for most problems. However, robust and efficient algorithms have been developed with practical applications in physics, genetics and phytomedicine, market research and marketing, performance evaluation, behavior analysis, as well as computer vision.

Timeline

Further Resources

Title
Author
Link
Type
Date

ARCHETYPAL ANALYSIS

Adele Cutler, Leo Breiman

Adademic paper

Archetypal analysis for machine learning and data mining

Morten Morup, Lars Kai Hansen

Web

Making Archetypal Analysis Practical

Christian Bauckhage, Christian Thurau

PDF

References

Golden logo
By using this site, you agree to our Terms & Conditions.