From 0a1ad07219daa52419eb3bdfbf435eeb1266e209 Mon Sep 17 00:00:00 2001 From: Vasil Zlatanov Date: Wed, 12 Dec 2018 22:56:49 +0000 Subject: Remove part1 --- part1/report/metadata.yaml | 17 ----------------- 1 file changed, 17 deletions(-) delete mode 100755 part1/report/metadata.yaml (limited to 'part1/report/metadata.yaml') 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). -... - -- cgit v1.2.3-54-g00ecf