# Universal and Automatic Elbow Detection for Learning the Effective Number of Components in Model Selection Problems

arXiv: https://arxiv.org/abs/2308.09102 · HTML: https://mlsanity.com/paper/2308.09102/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [Effective sample size approximations as entropy measures](https://mlsanity.com/paper/2602.22954/) (2026)

## Abstract (arXiv)

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.