How smoothing the affinity matrix affects neighborhood preservation in t-SNE
Read the original on arXiv Machine Learning →The paper investigates how adjusting the sharpness of t‑SNE’s affinity matrix influences neighborhood preservation across scales. By applying a row‑wise power transform parameterized by γ, the authors can smooth or sharpen each row while keeping sparsity and rank order intact, effectively rescaling the Gaussian bandwidth and altering local perplexities. Experiments show that sharpening enhances the retention of the very nearest neighbors, whereas smoothing improves the preservation of broader local neighborhoods, outperforming existing multiscale affinity methods in the mid‑local range.
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