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authorPaweł Redman <pawel.redman@gmail.com>2026-06-14 18:52:46 +0200
committerPaweł Redman <pawel.redman@gmail.com>2026-06-14 18:52:46 +0200
commit8ce3589d02e14322fc9aa7fe647a6f4f66a4eb59 (patch)
tree6e3b0989f171c7c14d8d6f3e432ea0571d1e2160 /site/content/gsoc/otsu2018_3.typ
parent89a67854f5af72836498a9412f759d4337fad32d (diff)
Fix typos in migrated GSoC posts
Co-authored-by: Cursor <cursoragent@cursor.com>
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date: datetime(year: 2020, month: 7, day: 26),
)
-In the #link-self("gsoc/otsu2018_2")[previous post] I explained how PCA can be used to create a convenient basis for representing real-world reflectivities. The procedure uses a set of measured spectra to compute an orthonormal basis, sorted by variance or "statistical significance." All but the three most significant vectors are then truncated. I called the difference between the actual spectrum and the reconstructed error term $bold(epsilon)$.
+In the #link-self("gsoc/otsu2018_2")[previous post] I explained how PCA can be used to create a convenient basis for representing real-world reflectivities. The procedure uses a set of measured spectra to compute an orthonormal basis, sorted by variance or "statistical significance." All but the three most significant vectors are then truncated. I called the difference between the actual spectrum and the reconstructed spectrum the error term $bold(epsilon)$.
Naturally the smaller the error, the better. Because $bold(epsilon)$ is a vector, the authors chose the Euclidean norm (2-norm or L2) to represent the (scalar) error:
$