# Principal Component Analysis

Patterns = principal component = vector
Finds major axis of variation in data
Each data point expressed as a linear combination of patterns

$Ax=\lambda x$
$\text{Matrix}\times\text{eigenvector}=\text{eigenvalue}\times\text{eigenvector}$
Eigenvectors capture major direction that are inherent in the matrix
The larger the eigenvalue, the more important is the vector
Covariance matrix contains terms for all positive pairs of features

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