Year: 2004
Type: article
Abstract: A familiar problem in machine learning is to determine which data points are outliers when the underlying distribution is unknown. In this paper, we adapt a simple algorithm from Zhou et al[3], designed for semisupervised learning, and show that it not only can automatically detect outliers by using local and global consistency of data points, but also automatically select optimal learning parameters, as well as predict class outliers for points introduced after training.
Authors: Adam Vinueza, Gregory Z. Grudić
Institution: University of Colorado Boulder
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2728558514