On the Use of Correlations to Interpret Canonical Functions
本文考察了两种典型函数（典型判别函数和典型相关变量）的系数，指出将系数转换为相关性会失去变量间联合贡献的信息，仅重复单变量统计结果，对评估变量重要性无益。
Two major types of canonical functions are considered, canonical discriminant functions that separate groups of observation vectors, and canonical variates associated with canonical correlations. The coefficients in both types of canonical functions reflect the joint contribution of the variables to the canonical functions. If the coefficients are converted to correlations, however, they merely reproduce univariate statistic values and become useless in gauging the importance of each variable in the context of the others.