Description: High-dimensional anomaly detection refers to a set of techniques specifically designed to identify unusual or atypical patterns in datasets that possess a large number of dimensions or features. In the context of artificial intelligence, these techniques are crucial as real-world data is often complex and multidimensional, making it difficult to identify anomalies using traditional methods. High dimensionality can lead to issues such as the curse of dimensionality, where the distance between data points becomes less meaningful, complicating outlier detection. Techniques for high-dimensional anomaly detection include machine learning algorithms such as isolation forests, support vector machines, and neural networks, which can learn complex patterns and detect deviations. These techniques not only help improve accuracy in anomaly identification but also allow for a better understanding of the underlying structure of the data. The relevance of these techniques extends across various industries, from fraud detection to real-time system monitoring, where early anomaly identification can prevent significant losses and enhance decision-making.
