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The solutions to these equations are eigenvalues <math>\lambda_1 \lambda_2 \cdots \lambda_m</math>. Often only <math>k m </math> eigenvalues will have a nonzero value, meaning that the inherent dimensionality of the data is <math>k</math>, being <math>n-k</math> dimensions noise. | The solutions to these equations are eigenvalues <math>\lambda_1 \lambda_2 \cdots \lambda_m</math>. Often only <math>k m </math> eigenvalues will have a nonzero value, meaning that the inherent dimensionality of the data is <math>k</math>, being <math>n-k</math> dimensions noise. | ||
| − | In order to represent the data in the k dimensional space we first construct the matrix E=[\vec{e_1} \vec{e_2} \cdots \vec{e_k}] | + | In order to represent the data in the k dimensional space we first construct the matrix <math>E=[\vec{e_1} \vec{e_2} \cdots \vec{e_k}]</math> |
Revision as of 01:08, 18 April 2008
The PCA, or Principal Component Analysis finds an orthonormal basis that best represents the data. The PCA diagonalizes the maximum likelihood estimate of the covariance matrix
$ C=\frac{1}{n} \sum_{i=1}^{n} \vec{x_i}\vec{x_i}^T $
by solving the eigenvalue equation
$ C\vec{e} = \lambda \vec{e} $
The solutions to these equations are eigenvalues $ \lambda_1 \lambda_2 \cdots \lambda_m $. Often only $ k m $ eigenvalues will have a nonzero value, meaning that the inherent dimensionality of the data is $ k $, being $ n-k $ dimensions noise.
In order to represent the data in the k dimensional space we first construct the matrix $ E=[\vec{e_1} \vec{e_2} \cdots \vec{e_k}] $
