基于旋转主成分分析的颜色组分预测研究

针对传统主成分分析法在建立光谱色彩空间时的“负指标”问题，提出了一种基于旋转主成分分析的颜色组分预测方法。该方法在最大程度保持主成分对原始光谱空间信息的累积方差贡献率同时，将初始特征向量转换为一组可作为实际基础颜色组分的全正向量，这些特征向量中的元素应按列向0或1分化。实验结果表明，这种新的预测方法不仅能很好地揭示目标图像的真实颜色成分，而且还具有较高的光谱数据重构精度。

Color Components Prediction Based on Rotated Principal Component Analysis

Aiming at the problem of negative index in the spectral color space built by means of traditional principal component analysis, a method of color components prediction based on rotated principal component analysis (RPCA) is proposed, which performs the linear transformation from initial eigenvectors to a set of all-positive vectors as the physical basis color components while retaining the cumulative ratio of the variance contributions of principal components to the original spectral space information to the maximum extent. The rotated column vectors should be also polarized between zero and unity. The experimental results show that the novel method of prediction not only uncovers the real color components of the target image better but reconstructs the normalized spectra data set with a high colorimetric and spectral accuracy.