Capital Mahalanobis

1 files changed, 5 insertions, 5 deletions

diff --git a/report/paper.md b/report/paper.md
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@@ -113,16 +113,16 @@ improve identification accuracy, and consider it an additional baseline.
 
 ## Mahalanobis Distance
 
-We were not able to achieve significant improvements using mahalanobis for 
+We were not able to achieve significant improvements using Mahalanobis for 
 original distance ranking compared to square euclidiaen metrics. 
 
-The mahalanobis distance metric was used to create the ranklist as an alternative to euclidean distance:
+The Mahalanobis distance metric was used to create the ranklist as an alternative to euclidean distance:
 
 $$ d_M(p,g_i) = (p-g_i)^TM(p-g_i). $$
 
-When performing mahalanobis with the covariance matrix $M$ generated from the training set, reported accuracy is reduced to **38%** .
+When performing Mahalanobis with the covariance matrix $M$ generated from the training set, reported accuracy is reduced to **38%** .
 
-We also attempted to perform the same mahalanobis metric on a reduced PCA featureset. This allowed for significant execution 
+We also attempted to perform the same Mahalanobis metric on a reduced PCA featureset. This allowed for significant execution 
 time improvements due to the greatly reduced computation requierments for smaller featurespace, but nevertheless demonstrated no
 improvements over an euclidean metric.
 
@@ -140,7 +140,7 @@ transformations performed the the ResNet-50 convolution model the features were
 \end{center}
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-While we did not use mahalanobis as a primary distance metric, it is possible to use the Mahalanobis metric, together with the next investigated solution involving $k$-reciprocal re-ranking.
+While we did not use Mahalanobis as a primary distance metric, it is possible to use the Mahalanobis metric, together with the next investigated solution involving $k$-reciprocal re-ranking.
 
 # Suggested Improvement