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| author | Yuchen Pei <me@ypei.me> | 2019-01-04 15:12:46 +0100 |
|---|---|---|
| committer | Yuchen Pei <me@ypei.me> | 2019-01-04 15:12:46 +0100 |
| commit | b801aab95dd9e0610df08db5c256879047ddfa31 (patch) | |
| tree | 0dfdca3fd6236c75f2781f925bb8437f2a94056c /posts | |
| parent | d61a0dadd70965c6d35a683c482ebcc385f662b1 (diff) | |
added a remark about scikit-learn
Diffstat (limited to 'posts')
| -rw-r--r-- | posts/2019-01-03-discriminant-analysis.md | 5 |
1 files changed, 5 insertions, 0 deletions
diff --git a/posts/2019-01-03-discriminant-analysis.md b/posts/2019-01-03-discriminant-analysis.md index 3e732db..51630b3 100644 --- a/posts/2019-01-03-discriminant-analysis.md +++ b/posts/2019-01-03-discriminant-analysis.md @@ -176,6 +176,11 @@ will result in a lossy compression / regularisation equivalent to doing analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) on $(M - \bar x) V_x D_x^{-1}$. +Note that as of 2019-01-04, in the scikit-learn implementation, the prediction is done without +any lossy compression, even if the parameter `n_components` is set to be smaller +than dimension of the affine space spanned by the centroids. +In other words, the prediction does not change regardless of `n_components`. + ### Fisher discriminant analysis The Fisher discriminant analysis involves finding an $n$-dimensional |