volume 159, issue 9, P2844-2855 2012
DOI: 10.1016/j.combustflame.2012.04.004
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Abstract: Principal component analysis (PCA) has been successfully applied to the analysis of combustion data-sets. However using PCA on a raw direct numerical simulation or an experimental data-set is not straightforward. Indeed, those datasets usually show non homogenous data density, hot and cold zones being generally over represented. This can introduce bias in the PCA reconstruction, especially when strong non-linear relationships characterize the data sample. To tackle this problem, a combination of the kernel den…

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