This is achieved by taking the max value of each entry of the corresponding indexes.
the resulting vector is closer to neighbors of both points, shrinking the distances between neighbors of both utterances.Moreover, because each feature (dimension) in the space is a representative utterance, after the collapsing of two utterances, we perform PCA to come up with the new dimensions after the user is done with the labeling.