Titlebook: Applied Machine Learning; David Forsyth Textbook 2019 Springer Nature Switzerland AG 2019 machine learning.naive bayes.nearest neighbor.SV

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Ambiguous

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

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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

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.

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PAEAN