diff options
| -rw-r--r-- | report2/README.md | 4 | ||||
| -rwxr-xr-x | report2/paper.md | 2 |
2 files changed, 4 insertions, 2 deletions
diff --git a/report2/README.md b/report2/README.md index 0e43ccf..92f592d 100644 --- a/report2/README.md +++ b/report2/README.md @@ -2,7 +2,7 @@ usage: evaluate.py [-h] [-t] [-c] [-k] [-m] [-e] [-r] [-p RERANKA]
[-q RERANKB] [-l RERANKL] [-n NEIGHBORS] [-v] [-s SHOWRANK]
[-1] [-M MULTRANK] [-C COMPARISON] [--data DATA] [-K KMEAN]
- [-P]
+ [-P] [-2 PCA]
optional arguments:
-h, --help show this help message and exit
@@ -45,4 +45,6 @@ optional arguments: '$size' -ARGUMENT REQUIRED, default=0-
-P, --mAP Display Mean Average Precision for ranklist of size -n
'$size'
+ -2 PCA, --PCA PCA Use PCA with -2 '$n_components' -ARGUMENT REQUIRED,
+ default=0-
```
diff --git a/report2/paper.md b/report2/paper.md index 98915e8..68b803a 100755 --- a/report2/paper.md +++ b/report2/paper.md @@ -81,7 +81,7 @@ This is due to the fact that the feature vectors appear scaled, releative to the significance, for optimal distance classification, and as such normalising loses this scaling by importance which has previously been introduced to the features. -## kMean Clustering +## kMeans Clustering An addition considered for the baseline is *kMeans clustering*. In theory this method allows to reduce computational complexity of the baseline NN by forming clusters and |