# RECol: Reconstruction Error Columns for Outlier Detection

arXiv: https://arxiv.org/abs/2102.02791 · HTML: https://mlsanity.com/paper/2102.02791/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)

## Built on by (most cited first, 5 of 5)

- [Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data](https://mlsanity.com/paper/2404.14933/) (2024)
- [Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking](https://mlsanity.com/paper/2504.20900/) (2025)
- [DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities](https://mlsanity.com/paper/2104.15052/) (2021)
- [Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data](https://mlsanity.com/paper/2504.20862/) (2025)
- [TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data](https://mlsanity.com/paper/2606.11844/) (2026)

## Abstract (arXiv)

Detecting outliers or anomalies is a common data analysis task. As a sub-field of unsupervised machine learning, a large variety of approaches exist, but the vast majority treats the input features as independent and often fails to recognize even simple (linear) relationships in the input feature space. Hence, we introduce RECol, a generic data pre-processing approach to generate additional columns in a leave-one-out-fashion: For each column, we try to predict its values based on the other columns, generating reconstruction error columns. We run experiments across a large variety of common baseline approaches and benchmark datasets with and without our RECol pre-processing method and show that the generated reconstruction error feature space generally seems to support common outlier detection methods and often considerably improves their ROC-AUC and PR-AUC values.