We design a Universal Automatic Elbow Detector (UAED) for deciding the effective number of components in model selection problems. The relationship with the information criteria widely employed in the literature is also discussed. The proposed UAED does not require the knowledge of a likelihood function and can be easily applied in diverse applications, such as regression and classification, feature and/or order selection, clustering, and dimension reduction. Several experiments involving synthetic and real data show the advantages of the proposed scheme with benchmark techniques in the literature.

翻译：我们设计了一种通用自动化肘部检测器（UAED），用于确定模型选择问题中的有效分量数量。本文还讨论了该方法与文献中广泛采用的信息准则之间的关系。所提出的UAED无需了解似然函数，可轻松应用于多种场景，例如回归与分类、特征和/或阶次选择、聚类以及降维。涉及合成数据与真实数据的多项实验表明，该方案相比文献中的基准技术具有优势。