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

翻译：本文定义了一种新的探索性技术——双原型分析。我们将原型分析进行扩展，以同时寻找观测和特征的原型。这一新型无监督机器学习工具的核心思想是，通过纯类型实例（双原型）来表示观测和特征，由于这些双原型是观测与特征的混合体，因而易于解释。此外，观测和特征被表示为双原型的混合，这也有助于理解数据的内在结构。我们提出了一种求解双原型分析的算法。研究表明，与双聚类相比，双原型分析在可解释性方面具有显著优势，这是因为双原型属于极端实例，而非双聚类返回的质心，这种特性更有利于人类的理解。通过在多个机器学习问题上的应用，我们验证了双原型分析的实际价值。