Criteria for evaluating dimension-reducing components for multivariate data

Principal components are the benchmark for linear dimension reduction, but they are not always easy to interpret. For this reason, some alternatives have been proposed in recent years. These methods produce components that, unlike principal components, are correlated and/or have nonorthogonal loadings. This article shows that the criteria commonly used to evaluate principal components are not adequate for evaluating such alternatives, and proposes two new criteria that are more suitable for this purpose.