The vector rejection is orthogonal to the projection, and intuitively, is the original vector after the projection has been removed: Using the vector rejection, we take a learned Hfeature detectorp, and discard these features from each of the word vectors.
for every data point H, w , we replace it by its vector rejection and rescale it to unit magnitude: A new classifier trained on the H i+1 , w i+1 data must now learn a different decision plane thanp, aŝ p is no longer present in any data points.