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In the PCA setup shown here, the data matrix X is organized such that its rows are data points and its columns are features. Each row is treated as a D-dimensional vector.

Conditions: Rows correspond to data points.; Columns correspond to features.; Each row lies in $\mathbb{R}^D$.

In the PCA setup shown here, the data matrix X is organized such that its rows are data points and its columns are features. Each row is treated as a D-dimensional vector.

Conditions: Rows correspond to data points.; Columns correspond to features.; Each row lies in $\mathbb{R}^D$.