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A new exploratory technique called biarchetype analysis is defined. We extendarchetype analysis to find the archetypes of both observations and featuressimultaneously. The idea of this new unsupervised machine learning tool is torepresent observations and features by instances of pure types (biarchetypes)that can be easily interpreted as they are mixtures of observations andfeatures. Furthermore, the observations and features are expressed as mixturesof the biarchetypes, which also helps understand the structure of the data. Wepropose an algorithm to solve biarchetype analysis. We show that biarchetypeanalysis offers advantages over biclustering, especially in terms ofinterpretability. This is because byarchetypes are extreme instances as opposedto the centroids returned by biclustering, which favors human understanding.Biarchetype analysis is applied to several machine learning problems toillustrate its usefulness.

Biarchetype analysis: simultaneous learning of observations and features based on extremes