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Classical dimensionality‑reduction methods either assume linearity (PCA) or rely on stochastic neighbor graphs (t‑SNE, UMAP) that are infeasible for UHD data. Recent deep learning approaches (variational auto‑encoders, contrastive learning) improve expressivity but still demand memory, where N is the number of samples and D the original dimension.

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