Thus, 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.
whenever a "Cannot-Link" constraint is provided, the similarity score between the two utterances is set to zero in the corresponding entry in SimMatrix.