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Unsupervised PCA paired with Random Forest beats supervised LDA-based pipelines and SVMs in hyperspectral classification, challenging the assumption that class-aware dimensionality reduction is strictly necessary for high-dimensional remote sensing.
Heavy-tailed state-space modeling can dramatically improve imaging fidelity in environments plagued by radio-frequency interference.
