Signal cancellation provides a radically new and efficient approach to exploratory factor analysis, without matrix decomposition nor presetting the required number of factors. Its current implementation requires that each factor has at least two unique indicators. Its principle is that it is always possible to combine two indicator variables exclusive to the same factor with weights that cancel their common factor information. Successful combinations, consisting of nose only, are recognized by their null correlations with all remaining variables. The optimal combinations of multifactorial indicators, though, typically retain correlations with some other variables. Their signal, however, can be cancelled through combinations with unifactorial indicators of their contributing factors. The loadings are estimated from the relative signal cancellation weights of the variables involved along with their observed correlations. The factor correlations are obtained from those of their unifactorial indicators, corrected by their factor loadings. The method is illustrated with synthetic data from a complex six-factor structure that even includes two doublet factors. Another example using actual data documents that signal cancellation can rival confirmatory factor analysis.

翻译：信号抵消为探索性因子分析提供了一种全新且高效的方法，无需进行矩阵分解或预先设定所需因子数量。其当前实现要求每个因子至少有两个独特指标。其原理在于，总能将归属于同一因子的两个指示变量通过权重组合，从而抵消它们的公共因子信息。成功的组合（仅包含噪声）可通过与所有剩余变量的零相关来识别。然而，多因子指标的最优组合通常会保留与其他变量的相关性。但其信号可通过与贡献因子的单因子指标进行组合而抵消。因子载荷根据所涉及变量的相对信号抵消权重及其观测到的相关性进行估计。因子间的相关性则通过其单因子指标的相关性，并经因子载荷校正后获得。该方法通过一个复杂的六因子结构（包含两个双因子对）的合成数据进行了说明。另一个使用实际数据的示例表明，信号抵消可与验证性因子分析相媲美。