I arrived at a point where I have eigenvectors and eigenvalues, therefore I can compute the projection matrix. What would be the next computational steps for determining a new data is an outlier or not? Supposing I plotted my current data points into the new projection space, how would I determine a new data point belongs to the group or not? Thank you.

P.S. as a concrete example, let's take this tutorial in Youtube https://www.youtube.com/watch?v=_UVHneBUBW0 How to determine a new Cell is an outlier or not?

My guess, intuitively: project the new data point into the PC space. Then, either calculate the distance to the cluster center OR find the closest point and see if it's not too far. What else? And how (both intuitively and formally)

  • $\begingroup$ The video does not seem to be talking about outlier detection at all. $\endgroup$ – amoeba Sep 16 '16 at 0:18
  • $\begingroup$ Right, I mean taking the cell example from the video, how would it be continued with outlier detection? $\endgroup$ – Madrugada Sep 16 '16 at 2:35
  • $\begingroup$ See related question stats.stackexchange.com/q/71899/3277 $\endgroup$ – ttnphns Sep 16 '16 at 3:55

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