数量
发表于 2025-3-30 10:32:56
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Ambiguous
发表于 2025-3-30 15:10:34
High Dimensional Data is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean). The covariance matrix may be less familiar. This is a collection of all covariances between pairs of components. We use covaria
拍翅
发表于 2025-3-30 16:48:18
Principal Component Analysistem, we can set some components to zero, and get a representation of the data that is still accurate. The rotation and translation can be undone, yielding a dataset that is in the same coordinates as the original, but lower dimensional. The new dataset is a good approximation to the old dataset. All
爵士乐
发表于 2025-3-30 22:31:13
Low Rank Approximationsate points. This data matrix must have low rank (because the model is low dimensional) . it must be close to the original data matrix (because the model is accurate). This suggests modelling data with a low rank matrix.
易于出错
发表于 2025-3-31 01:40:23
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PAEAN
发表于 2025-3-31 06:32:09
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