Question

Show that $\frac{\sum^k_{i=1}\text{Var}(Y_i)}{\sum^p_{i=1}\text{Var}(X_i)}=\frac{\sum^k_{i=1}\lambda_i}{\sum^p_{i=1}\lambda_i}$

score 44 · Answer 1 · 2021-05-12 11:53:35

44

Views

Show that $\frac{\sum^k_{i=1}\text{Var}(Y_i)}{\sum^p_{i=1}\text{Var}(X_i)}=\frac{\sum^k_{i=1}\lambda_i}{\sum^p_{i=1}\lambda_i}$

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71

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124

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List Question

Show that $\frac{\sum^k_{i=1}\text{Var}(Y_i)}{\sum^p_{i=1}\text{Var}(X_i)}=\frac{\sum^k_{i=1}\lambda_i}{\sum^p_{i=1}\lambda_i}$

PCA-algorithm dependence on number of principal components.

Covariance matrix in PCA approach

What does SCoTLASS stand for

Alternative definition of principal component analysis

Bound for a random matrix and a normal distributed random vector

How to create separation of "sides"/signs in PCA / eigenvector "directionality of data" analysis?

What is the correct formula for the covariance matrix?

Help with a PCA procedure.

In PCA, how are maximising variance and minimising reconstruction error equivalent?

Singular Vector Decomposition and PCA interpretation

unit vector after transformation with longer length than the eigenvector with highest eigenvalue

compute eigendecomposition to find eigenvalues and eigenvector

Why is it possible to write the following term when using the summation solution to PCA?

Confused about the relationship between PCA and robust PCA

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