Outlier Detection for Text Data
By Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park. 2017
50 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Networks and Communications, Natural Language Processing, Anomaly & Outlier Detection
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Douwe Kiela
- Mohit Bansal
- Nazneen Rajani
- Tristan Thrush
- Yacine Jernite
- Yonatan Belinkov
- Yejin Choi
- Margaret Mitchell
In Computer Networks and Communications
In Natural Language Processing
- Tristan Thrush
- Douwe Kiela
- Nazneen Rajani
- Sameep Mehta
- Angelina McMillan-Major
- Yacine Jernite
- Alexandra Sasha Luccioni
- Margaret Mitchell
Citations in the corpus
- Measuring Data (2022, background): “They can be useful for initial, high-level analyses of datasets and as indicators to guide further fine-grained analyses which can go deeper in terms of the intrinsic characteristics of outliers and representative samples in the dataset (e.g., (Vig et al., 2021; Kannan et al., 2017)).”
- Overview and Importance of Data Quality for Machine Learning Tasks (2020, methodology): “We will discuss the challenges for detecting outliers in text data and cover solutions proposed in [10, 12].”