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Can you help me find the name of this classification method:

Assume we have $n$ dimensional feature vectors we want to classify in two classes.

  1. We model the classes as two $n$ dimensional gaussian distributions estimated from the data.
  2. We classify a new vector to the class that maximizes the PDF (probability density function) at that point.
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    $\begingroup$ Two component Gaussian mixture model? $\endgroup$
    – Bey
    Jan 14 '19 at 3:44
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Probably Quadratic Discriminant Analysis.

There are also names for different constraints you could make:

  1. Covariance matrices of both classes are equal - Linear Discriminant Analysis.

  2. Only diagonal elements of the covariance matrix are non-zero - Naive Bayes Classifier

  3. Covariance matrix is identity (diagonals = 1, non-diagonals = 0) - Nearest Centroid Classifier

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