  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.
