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| author | Vasil Zlatanov <vasko@e4-pattern-vm.europe-west4-a.c.electric-orbit-223819.internal> | 2018-12-05 16:36:15 +0000 |
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| committer | Vasil Zlatanov <vasko@e4-pattern-vm.europe-west4-a.c.electric-orbit-223819.internal> | 2018-12-05 16:36:15 +0000 |
| commit | e42170b70bb9710d73ff22fcd06ae8724a78cbd1 (patch) | |
| tree | 3edc5777e62537b1c79140d89b648b3829564b68 /part1/report/metadata.yaml | |
| parent | bcd380b631184e9d4e58c0aa80afb17727581066 (diff) | |
| download | vz215_np1915-e42170b70bb9710d73ff22fcd06ae8724a78cbd1.tar.gz vz215_np1915-e42170b70bb9710d73ff22fcd06ae8724a78cbd1.tar.bz2 vz215_np1915-e42170b70bb9710d73ff22fcd06ae8724a78cbd1.zip | |
Move part1 parts to seperate folder
Diffstat (limited to 'part1/report/metadata.yaml')
| -rwxr-xr-x | part1/report/metadata.yaml | 17 |
1 files changed, 17 insertions, 0 deletions
diff --git a/part1/report/metadata.yaml b/part1/report/metadata.yaml new file mode 100755 index 0000000..5c4dde1 --- /dev/null +++ b/part1/report/metadata.yaml @@ -0,0 +1,17 @@ +--- +title: 'EE4-68 Pattern Recognition (2018-2019) CW1' +author: + - name: Vasil Zlatanov (01120518), Nunzio Pucci (01113180) + location: vz215@ic.ac.uk, np1915@ic.ac.uk +numbersections: yes +lang: en +babel-lang: english +abstract: | + 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 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). +... + |