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enter image description hereThe figure below shows the PCA projections of inputs which are 14 meteorological features, (i.e. wind, temperature, humidity, pressure, and so on.) I would like to use any technique to make it more separable than this, The ISOMAP method is also used instead of PCA, but it gave a non-separable distribution as well! Any suggestions would be highly appreciated.

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    $\begingroup$ have you considered trying to use an autoencoder? $\endgroup$
    – spektr
    May 22 '16 at 14:51
  • $\begingroup$ Excuse my naivety...but what is an autoencoder? $\endgroup$
    – mhdella
    May 22 '16 at 21:07
  • $\begingroup$ An autoencoder is a nonlinear approach to dimensionality reduction. It's an unsupervised learning technique that can, in its simplest form, just be a feed forward neural network formed a certain way. I would recommend googling it. $\endgroup$
    – spektr
    May 22 '16 at 21:10
  • $\begingroup$ Thanks choward for your swift response and clarification.... I hope autoencoder would be better than what isomap was.... $\endgroup$
    – mhdella
    May 22 '16 at 21:16
  • $\begingroup$ The point of these methods is to make data more separable, conditional on the fact they can be separated. I don't know much about ISOMAP, but I know an autoencoder can fair well for these types of tasks. Hopefully your data can actually be separated like you wish. $\endgroup$
    – spektr
    May 22 '16 at 21:26

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