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authornunzip <np.scarh@gmail.com>2018-11-20 14:55:37 +0000
committernunzip <np.scarh@gmail.com>2018-11-20 14:55:37 +0000
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parentbbaa35eac0020524087f3a867cbad68a43c9b7eb (diff)
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Merge branch 'master' of skozl.com:e4-pattern
-rwxr-xr-xreport/metadata.yaml13
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).
...