I am trying to understand the intuition behind the idea of finding a hyperplane that separates the training data from the origin in the feature space.

Why separation from origin with a hyperplane helps with outlier detection?

Is the origin the 0-vector in the feature space?

Any hint/assistance is appreciated.


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