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authornunzip <np_scarh@e4-pattern-vm.europe-west4-a.c.electric-orbit-223819.internal>2018-12-05 19:12:56 +0000
committernunzip <np_scarh@e4-pattern-vm.europe-west4-a.c.electric-orbit-223819.internal>2018-12-05 19:12:56 +0000
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-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).
-...
-