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This paper evaluates different multivariate outlier detection methods (Z-score, Mahalanobis distances, PCA, Isolation Forest, and Hotelling's T-squared) for identifying irregular operations in district heating system data, proposing an ensemble approach based on agreement among the best-performing methods.
A discussion thread on multivariate probability models in machine learning.
This blog post explores the intersection of machine learning and neuroscience, specifically focusing on using multivariate classification techniques on neuroimaging data to understand brain function and behavior.