| Revision as of 16:15, 10 September 2004 edit81.139.11.17 (talk) →See also: * Principal components analysis← Previous edit | Revision as of 21:53, 23 September 2004 edit undoDcljr (talk | contribs)Extended confirmed users20,166 edits specified math-stub; edited first sentence; should this be its own article? see "White noise"Next edit → | ||
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| In ], a random vector is said to be "white" if |
In ], a random ] is said to be "white" if its elements are uncorrelated and have unit variance. This corresponds to a flat ]. | ||
| A vector can be '''whitened''' to remove these correlations. This is useful in various procedures such as ]. | A vector can be '''whitened''' to remove these correlations. This is useful in various procedures such as ]. | ||
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| where X is the matrix to be whitened, E is the column matrix of Eigenvectors and A' is the transposed diagonal matrix of eigenvalues. | where X is the matrix to be whitened, E is the column matrix of Eigenvectors and A' is the transposed diagonal matrix of eigenvalues. | ||
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| == See also == | == See also == | ||
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| * ] | * ] | ||
| * ] | * ] | ||
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Revision as of 21:53, 23 September 2004
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In statistics, a random vector is said to be "white" if its elements are uncorrelated and have unit variance. This corresponds to a flat power spectrum.
A vector can be whitened to remove these correlations. This is useful in various procedures such as data compression.
Whitening a signal
- X_white = E * A' * X
where X is the matrix to be whitened, E is the column matrix of Eigenvectors and A' is the transposed diagonal matrix of eigenvalues.
See also
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