Mahalanobis Distance & Multivariate Outlier Detection in R | Built In
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Mahalanobis Distance & Multivariate Outlier Detection in R | Built In
Mahalanobis distance is a distance metric that finds the distance between a point and a distribution. It’s effective for analyzing multivariate outliers and data.
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Mahalanobis distance - SlideServe
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SOLVED:Let 𝐒 be the covariance matrix of the data. The Mahalanobis distance between 𝐱^a and 𝐱^b is defined as (𝐱^a-𝐱^b)^⊤ 𝐒^-1(𝐱^a-𝐱^b) Explain how to approximate this distance using M-dimensional PCA.
SOLVED:Let 𝐒 be the covariance matrix of the data. The Mahalanobis distance between 𝐱^a and 𝐱^b is defined as (𝐱^a-𝐱^b)^⊤ 𝐒^-1(𝐱^a-𝐱^b) Explain how to approximate this distance using M-dimensional PCA.
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Mahalanobis distance - SlideServe
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SOLVED:Let 𝐒 be the covariance matrix of the data. The Mahalanobis distance between 𝐱^a and 𝐱^b is defined as (𝐱^a-𝐱^b)^⊤ 𝐒^-1(𝐱^a-𝐱^b) Explain how to approximate this distance using M-dimensional PCA.
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