Unsupervised Learning Approaches for Anomaly Detection in High-Dimensional Data

Abstract

Anomaly detection in high-dimensional data presents significant challenges due to the curse of dimensionality and the complexity of identifying deviations from normal patterns. This paper explores various unsupervised learning approaches for addressing these challenges. We review and compare methods including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Isolation Forest, and Autoencoders. Each approach is evaluated based on its ability to detect anomalies without prior labeled data, focusing on effectiveness, scalability, and interpretability. We also present empirical results on benchmark datasets to highlight the strengths and limitations of these techniques. Our findings provide insights into the suitability of different unsupervised learning methods for various types of high-dimensional datasets and offer guidance for selecting appropriate approaches in practice.