I just get an idea, and I’m registering it on my blog just to let anyone know this is my idea, also if you want to work on it, it’s ok for me…
Fuzzy-Principal Component Analysis (FPCA), as we know a normal PCA give us a vector that characterizes a matrix (in my case, an image), but this vector is an specific value rounded by our computations, and on this calculus we can lose valuable information that can be useful to calculate the Euclidean distance between object n-dimensional. So, to avoid to loose information, we can fuzzy the output vectors rounded in a fuzzy manner.
Well that’s all, I think this can improve the efficiency of a classifier.
Any comments?
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