Quantifying the estimation error of principal component vectors
Principal component analysis is an important pattern recognition and dimensionality reduction tool in many applications. Principal components are computed as eigenvectors of a maximum likelihood covariance $\widehat{\Sigma}$ that approximates a population covariance $\Sigma$, and these eigenvectors...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2019
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