- Tytuł:
- A comparative study for outlier detection methods in high dimensional text data
- Autorzy:
- Park, Cheong Hee
- Powiązania:
- https://bibliotekanauki.pl/articles/2201316.pdf
- Data publikacji:
- 2023
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
curse of dimensionality
dimension reduction
high dimensional text data
outlier detection - Opis:
- Outlier detection aims to find a data sample that is significantly different from other data samples. Various outlier detection methods have been proposed and have been shown to be able to detect anomalies in many practical problems. However, in high dimensional data, conventional outlier detection methods often behave unexpectedly due to a phenomenon called the curse of dimensionality. In this paper, we compare and analyze outlier detection performance in various experimental settings, focusing on text data with dimensions typically in the tens of thousands. Experimental setups were simulated to compare the performance of outlier detection methods in unsupervised versus semisupervised mode and uni-modal versus multi-modal data distributions. The performance of outlier detection methods based on dimension reduction is compared, and a discussion on using k-NN distance in high dimensional data is also provided. Analysis through experimental comparison in various environments can provide insights into the application of outlier detection methods in high dimensional data.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 1; 5--17
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł