Adaptive femtosecond control experiments are expanding the possibilities for using laser pulses as photophysical and photochemical reagents. However, because of the large number of variables necessary to perform these experiments (usually 100-200), it has proven difficult to elucidate the underlying control mechanisms from the optimized pulse shapes. If adaptive control is to become a widespread tool for examining chemical dynamics, methods must be developed that reveal latent control mechanisms. This manuscript presents a generally applicable method for dimension reduction of adaptive control experiments based on partial least squares regression analysis (PLS) of the normalized covariance matrix of the total data set. When applied to experimental results obtained in our laboratory, it shows that only seven fundamental dimensions from an original 208-dimension search space are needed to account for approximately 90% of the variance in the observed fitness of 11,700 laser-pulse shapes explored during the optimization experiment. Furthermore, the seven dimensions have a remarkable regularity in their functional form. It is anticipated that this work will facilitate theoretical treatments directly linking the optimal fields to control mechanisms, allow quantitative comparisons of independent control results, and suggest new experimental methods for rapid adaptive searches.