Remember that component 1 is the principal component with the highest variance (since highest variance equates to highest potential signal).…
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Principal Component Analysis for Dimensionality Reduction
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A human brain simply can’t operate with that much information in a short period of time. At least my brain…
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PCA can reduce dimensionality but it wont reduce the number of features / variables in your data. What this means…
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Continue ReadingUnpacking (**PCA)
Staring with sample data as usual.rng = np.random.RandomState(1)X_raw = np.dot(rng.rand(2, 2), rng.randn(2, 200)).TX_mean = X_raw.mean(axis=0)X = X_raw – X_meanDefining the…
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