In the above approaches of SVD, Markov model and dynamical language model, there is a step to calculate the correlation-related distance between two genomes after removing the randomness or noise from the composition vectors. A drawback is that these correlation-related distances are not proper distance metrics in the strict mathematical sense (Professor Bailin Hao, personal communication, 2009; see also [21] ). There are some ways to overcome this problem. One way is to change the concept of distance to that of dissimilarity proposed by Xu and Hao [15] in the Markov model approach. Another way is to replace a pseudo-distance by a proper distance metric, which requires that the results are not worsened from the biological point of view. In the first way, there is no widely accepted mathematical definition for the concept of dissimilarity or similarity. Chen et al. [22] defined a similarity metric, but unfortunately the sample correlation between two vectors in a vector space does not yield a proper similarity under their definition.