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Graduate Mahalanobis Distance using Eigen-Values of the Covariance Matrix
Given the formula of Mahalanobis Distance: D^2_M = (\mathbf{x} - \mathbf{\mu})^T \mathbf{S}^{-1} (\mathbf{x} - \mathbf{\mu}) If I simplify the above expression using Eigen-value decomposition (EVD) of the Covariance Matrix: S = \mathbf{P} \Lambda \mathbf{P}^T Then, D^2_M =...- orajput
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- Covariance Covariance matrix Matrix
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- Forum: Linear and Abstract Algebra