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| author | nunzip <np_scarh@e4-pattern-vm.europe-west4-a.c.electric-orbit-223819.internal> | 2018-12-05 19:12:56 +0000 |
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| committer | nunzip <np_scarh@e4-pattern-vm.europe-west4-a.c.electric-orbit-223819.internal> | 2018-12-05 19:12:56 +0000 |
| commit | 54e0552d2f14e734809912ca0f4e7ffa1e8a682e (patch) | |
| tree | 0c7a2a7b66cf6c0200ee070f1dff1656f2bdfbc5 /part1/report/metadata.yaml | |
| parent | 60189ef2705ea441cadff4afd63fe396edb6550a (diff) | |
| parent | 219432c1bf2d9edd9fe7d2d9108627646447a0ec (diff) | |
| download | vz215_np1915-54e0552d2f14e734809912ca0f4e7ffa1e8a682e.tar.gz vz215_np1915-54e0552d2f14e734809912ca0f4e7ffa1e8a682e.tar.bz2 vz215_np1915-54e0552d2f14e734809912ca0f4e7ffa1e8a682e.zip | |
Resolve conflict lambda
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| -rwxr-xr-x | part1/report/metadata.yaml | 17 |
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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). +... + |