Make abstract short and good

1 files changed, 3 insertions, 10 deletions

diff --git a/report/metadata.yaml b/report/metadata.yaml
index c7ede78..b60aa7e 100755
--- a/report/metadata.yaml
+++ b/report/metadata.yaml
@@ -10,17 +10,10 @@ lang: en
 babel-lang: english
 abstract: |
   In this coursework we will analyze the benefits of different face recognition methods.
-  On one hand we will analyze PCA, Principal Components Analysis. This method
-  allows dimensionality reduction, obtaining a generative subspace which is very reliable for 
-  face reconstruction.
+  We analyze dimensionality reduction with PCA, obtaining a generative subspace which is very reliable for face reconstruction.  Furthermore, we evaluate LDA, which is able to perform reliable classification, generating a discriminative subspace, where separation of classes is easier to identify.
 
-  On the other hand LDA, Linear Discriminant Analysis, allows to perform a very reliable classification,
-  generating a discriminative subspace, in which the separation between classes is easier to recognize.
-
-  In the final part we will analyze the benefits of using a combined version of the two methods using Fisherfaces.
-  As we will see, the PCA-LDA ensemble will obtain much more accurate results with a very high speed of computation.
-
-  The data used includes 52 classes with 10 samples each. The number of features is 2576(since the size of the pictures is 46x56).
+  In the final part we analyze the benefits of using a combined version of the two methods using Fisherfaces and evaluate the benefits of ensemble learning with regards to data and feature space ranodmisation. We find that combined PCA-LDA obtains lower classification error PCA or LDA individually, while also maintaining a low computational costs, allowing us to take advantage of ensemble learning.
 
+  The dataset used includes 52 classes with 10 samples each. The number of features is 2576 (46x56).
 ...