![]() T-SNE can be used for dimensionality reduction for nonlinear datasets. PCA works better in revealing linear patterns in high-dimensional data but has limitations with the nonlinear dataset.PCA preserves the global data structure by forming well-separated clusters but can fail to preserve the.PCA helps to assess which original samples are similar and different from each other. Most of the variation, which is easy to visualize and summarise the feature of original high-dimensional datasets in ![]()
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