Numerical relationships of multi-feature data are widely concerned in data preprocessing,
but semantic interpretation of the features received less attention. We completely approve
of the importance of numerical relationships. However, in our opinion, the interpretative
relationships of the data should be important as well. In this paper, we regard the
principle component analysis (PCA) as a special case of numerical relationships. We
propose an interpretative division method on the PCA and its improved algorithms from an
explanatory perspective. Our method integrates the numerical data analysis with the
semantic understanding of the problem. Experiments are conducted on real data sets and our
method demonstrates good performance and outperforms the corresponding PCA algorithms. On
the real data sets of our experiments, we also find that the interpretative features with
small eigenvalues are better choices than the principle components of PCA.
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