diff options
| author | nunzip <np.scarh@gmail.com> | 2018-12-12 23:14:44 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2018-12-12 23:14:44 +0000 |
| commit | 7167c720b525b86d689775c58b0be4ec92c8fc4d (patch) | |
| tree | c5ab0df21723f161f67f2ac4970434798d569b55 /part1/report/metadata.yaml | |
| parent | 88ed5d2fec953107584fb53fefd9094dac6dec38 (diff) | |
| parent | 7eef0dccc6ff8cfa2e75fa3d940f9a2f38144611 (diff) | |
| download | vz215_np1915-7167c720b525b86d689775c58b0be4ec92c8fc4d.tar.gz vz215_np1915-7167c720b525b86d689775c58b0be4ec92c8fc4d.tar.bz2 vz215_np1915-7167c720b525b86d689775c58b0be4ec92c8fc4d.zip | |
Merge branch 'master' of git.skozl.com:e4-pattern
Diffstat (limited to 'part1/report/metadata.yaml')
| -rwxr-xr-x | part1/report/metadata.yaml | 17 |
1 files changed, 0 insertions, 17 deletions
diff --git a/part1/report/metadata.yaml b/part1/report/metadata.yaml deleted file mode 100755 index 5c4dde1..0000000 --- a/part1/report/metadata.yaml +++ /dev/null @@ -1,17 +0,0 @@ ---- -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). -... - |