目的 在 PCA 算法的基础上提出一种基于 LMS 锥响应加权的残差补偿光谱降维模型。方法介绍以 LMS 为加权函数对源光谱加权以及用残差光谱对模型补偿的基本框架。以 Munsell 色卡作为训练样本， 以多光谱图像和 SG 色卡为检测样本， 用文中算法与主成分分析算法分别对其进行降维、 重构。结果 在不同维数下， 采用文中算法重构都具有较高的色度精度， 该算法有效提高了主成分分析算法的色度精度， 且在变光源情况下仍具有较高的色度稳定性。结论 该降维算法采用LMS加权并对残差光谱补偿是一种精度较高的光谱降维模型。

On the basis of the principal component analysis algorithm, a residual compensation of weighted spectral dimension reduction model based on LMS was proposed in this paper. The basic framework for using the LMS weight function for weighting the original spectra and using the residual spectra for model compensation was introduced. The Munsell color cards were chosen as the training samples, while the SG color cards and multi-spectra images were chosen as the test samples. The RCwPCA proposed in this paper was compared with PCA in compressing and reconstructing the training and test samples. Experimental results showed that reconstruction by RCwPCA could reach higher chromaticity accuracy under different dimensions. This algorithm effectively improved the chromaticity accuracy of PCA and kept higher chromaticity stability under the condition of variable light sources. The RCwPCA dimension reduction model which used LMS weighting and residual spectra compensation was a spectral dimension reduction model with high precision.