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| -rwxr-xr-x | report/metadata.yaml | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/report/metadata.yaml b/report/metadata.yaml index b60aa7e..b404339 100755 --- a/report/metadata.yaml +++ b/report/metadata.yaml @@ -9,10 +9,10 @@ numbersections: yes lang: en babel-lang: english abstract: | - In this coursework we will analyze the benefits of different face recognition methods. - 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. + In this coursework we analyze the benefits of different face recognition methods. + We look at 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. - 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. + 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 than PCA or LDA individually, while also maintaining 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). ... |