Exercise:PCA and Whitening
From Ufldl
(→PCA, PCA whitening and ZCA implementation) |
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==== Step 1a: Implement PCA ==== | ==== Step 1a: Implement PCA ==== | ||
- | In this step, you will implement PCA to obtain <math>x_{rot}</math>, the matrix in which the data is "rotated" to the basis comprising the principal components (i.e. the eigenbasis of <math>\Sigma</math>). | + | In this step, you will implement PCA to obtain <math>x_{rot}</math>, the matrix in which the data is "rotated" to the basis comprising the principal components (i.e. the eigenbasis of <math>\Sigma</math>). Note that in this part of the exercise, you should ''not'' whiten the data. |
==== Step 1b: Check covariance ==== | ==== Step 1b: Check covariance ==== | ||
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==== Step 4a: Implement PCA with whitening and regularisation ==== | ==== Step 4a: Implement PCA with whitening and regularisation ==== | ||
- | Now implement PCA with whitening and regularisation to produce the matrix <math>x_{PCAWhite}</math> | + | Now implement PCA with whitening and regularisation to produce the matrix <math>x_{PCAWhite}</math> with the following parameters: |
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+ | epsilon = 1e-4 | ||
==== Step 4b: Check covariance ==== | ==== Step 4b: Check covariance ==== |