Adaptive Tensor Decomposition for Real-Time Anomaly Detection in High-Dimensional Data Streams
Keywords:
Tensor Decomposition, Anomaly Detection, High-Dimensional Data, Real-Time Analysis, Adaptive Algorithms, Machine Learning, Data Streams, Computational EfficiencyAbstract
In the burgeoning domain of computational science, the detection and interpretation of anomalies in high-dimensional data streams pose significant challenges. This study introduces an adaptive tensor decomposition framework designed to dynamically adjust to evolving data structures, ensuring real-time anomaly identification. Employing state-of-the-art machine learning algorithms, our method enhances detection accuracy while minimizing computational overhead. The results demonstrate significant advancements over existing models, offering novel insights into anomaly characterization necessary for critical decision-making processes.
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