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authorVasil Zlatanov <v@skozl.com>2018-11-20 14:52:59 +0000
committerVasil Zlatanov <v@skozl.com>2018-11-20 14:52:59 +0000
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Make abstract short and good
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diff --git a/report/metadata.yaml b/report/metadata.yaml
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@@ -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).
...