Empirically derived basis functions for unsupervised classification of radial profile data
We present an analysis of empirically derived basis vectors for feature detection in radial profile data. Our aim is to classify broad and peaked profiles using unsupervised techniques. Radial data often contains a continuum of profile shapes from broad to peaked, as such clustering methods may be unreliable. Previously, ad hoc heuristic measures had been used for classification of profiles from raw data (without tomographic reconstruction), which required significant manual inspection of the...[Show more]
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|Source:||Fusion Engineering and Design|
|01_Pretty_Empirically_derived_basis_2010.pdf||152.76 kB||Adobe PDF||Request a copy|
|02_Pretty_Empirically_derived_basis_2010.pdf||284.83 kB||Adobe PDF||Request a copy|
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