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 *** of a matrix represent observations of some sort and the *** of the matrix represent features or variables (but this is by no means a ***).
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 *** of a matrix represent observations of some sort and the *** of the matrix represent features or variables (but this is by no means a ***).
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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 *** of a matrix represent observations of some sort and the *** of the matrix represent features or variables (but this is by no means a ***).
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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