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Question

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the rows of a matrix represent *** of some sort and the columns of the matrix represent *** or *** (but this is by no means a ***).

Answer

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the rows of a matrix represent observations of some sort and the columns of the matrix represent features or variables (but this is by no means a requirement).

Question

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the rows of a matrix represent *** of some sort and the columns of the matrix represent *** or *** (but this is by no means a ***).

Answer

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Question

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the rows of a matrix represent *** of some sort and the columns of the matrix represent *** or *** (but this is by no means a ***).

Answer

One category of statistical dimension reduction techniques is commonly called principal components analysis (PCA) or the singular value decomposition (SVD). These techniques generally are applied in situations where the rows of a matrix represent observations of some sort and the columns of the matrix represent features or variables (but this is by no means a requirement).

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status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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