Rotation Invariant Feature Extraction in the Classification of Galaxy Morphologies
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2014-07-08
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en
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The Galaxy Zoo project is a crowdsourcing platform to classify the
morphology of galaxies into different categories. Recently, the project
set out a challenge to automatically predict these crowdsourced classifications
using machine learning techniques. In this thesis, one of
these machine learning techniques is explored and modified. This
technique, designed by Coates et al. [4], works by learning features in
an unsupervised manner using k-means. These features are rotation
sensitive, but since there is no up or down in space, the system would
ideally work rotation invariantly. Therefore, the system was modified
to account for rotation sensitivity in an efficient manner by changing
the distance metric that is used by the k-means algorithm. Results
show that this improves the performance significantly by more than
5%: the regular method achieves a root mean squared error of 0.10789
while the rotation invariant method achieves a score of 0.10256.
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Faculteit der Sociale Wetenschappen