Weighted and robust archetypal analysis

Authors:
Manuel J. A. Eugster;Friedrich Leisch
Affiliations:
-;-
Venue:
Computational Statistics & Data Analysis
Year:
2011

Citing 3
Cited 1

Robust regression and outlier detection

Robust regression and outlier detection
On the use of archetypes as benchmarks

Applied Stochastic Models in Business and Industry - Special issue on statistical methods in performance analysis
Making Archetypal Analysis Practical

Proceedings of the 31st DAGM Symposium on Pattern Recognition

Weighted archetypal analysis of the multi-element graph for query-focused multi-document summarization

Expert Systems with Applications: An International Journal

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Abstract

Archetypal analysis represents observations in a multivariate data set as convex combinations of a few extremal points lying on the boundary of the convex hull. Data points which vary from the majority have great influence on the solution; in fact one outlier can break down the archetype solution. The original algorithm is adapted to be a robust M-estimator and an iteratively reweighted least squares fitting algorithm is presented. As a required first step, the weighted archetypal problem is formulated and solved. The algorithm is demonstrated using an artificial example, a real world example and a detailed simulation study.