# Outlier Detection for Text Data : An Extended Version

arXiv: https://arxiv.org/abs/1701.01325 · HTML: https://mlsanity.com/paper/1701.01325/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2017.

## Built on by (most cited first, 8 of 8)

- [Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2104.07917/) (2021)
- [Deep Clustering based Fair Outlier Detection](https://mlsanity.com/paper/2106.05127/) (2021)
- [Measuring Data](https://mlsanity.com/paper/2212.05129/) (2022)
- [Text Classification Under Class Distribution Shift: A Survey](https://mlsanity.com/paper/2502.12965/) (2025)
- [Out-of-Category Document Identification Using Target-Category Names as Weak Supervision](https://mlsanity.com/paper/2111.12796/) (2021)
- [A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text](https://mlsanity.com/paper/2405.13031/) (2024)
- [IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation](https://mlsanity.com/paper/2108.00360/) (2021)
- [SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated](https://mlsanity.com/paper/2208.05561/) (2022)

## Abstract (arXiv)

The problem of outlier detection is extremely challenging in many domains such as text, in which the attribute values are typically non-negative, and most values are zero. In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data. In this paper, we present a matrix factorization method, which is naturally able to distinguish the anomalies with the use of low rank approximations of the underlying data. Our iterative algorithm TONMF is based on block coordinate descent (BCD) framework. We define blocks over the term-document matrix such that the function becomes solvable. Given most recently updated values of other matrix blocks, we always update one block at a time to its optimal. Our approach has significant advantages over traditional methods for text outlier detection. Finally, we present experimental results illustrating the effectiveness of our method over competing methods.