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| -rwxr-xr-x | report/paper.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/report/paper.md b/report/paper.md index 6c5ff9b..99e6836 100755 --- a/report/paper.md +++ b/report/paper.md @@ -96,7 +96,7 @@ in fig.\ref{fig:face10rec} with respective $M$ values of $M=10, M=100, M=200, M= It is visible that the improvement in reconstruction is marginal for $M=200$ and $M=300$. For this reason choosing $M$ larger than 100 gives very marginal returns. This is evident when looking at the variance ratio of the principal components, as the contribution they have is very low for values above 100. -With $M=100$ we are be able to reconstruct effectively 97% of the information from our initial training data. +With $M=100$ we are be able to reconstruct effectively 94% of the information from our initial training data. Refer to figure \ref{fig:eigvariance} for the data variance associated with each of the M eigenvalues. |