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ml-recall

#explained-variance

5 questions from Dimensionality Reduction

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5 questions · 5 min · score and explanations at the end

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Question 1: Short answer

easyAI-checkedDimensionality Reduction

A centered dataset with two features has the sample covariance matrix

C=[2112].\mathbf{C} = \begin{bmatrix} 2 & 1 \\ 1 & 2 \end{bmatrix}.

PCA keeps only the first principal component. What fraction of the total variance does that component explain?

a fraction between 0 and 1, e.g. 0.42