研究连续Morlet小波变换矩阵行矢量的线性相关性，这种相关性使得连续Morlet小波变换的结果存在很大的冗余，利用奇异值分解(Singular value decomposition, SVD)来压缩这种冗余性。理论分析表明，SVD技术可以将连续Morlet小波变换矩阵的信息完全压缩到少数的非零奇异值及其对应的正交奇异矢量中，分析压缩前后数据量的比例，证实矩阵维数越大，压缩效果越好。研究确定性信号和噪声的连续Morlet小波变换结果的奇异值的分布特点，发现确定性信号的有效奇异值数量由信号中的频率数量决定，有效奇异值之后的奇异值会很快地下降到零，而噪声的奇异值序列的变化比较均匀，下降速度比较缓慢。利用确定性信号和噪声奇异值的这种差异，可以实现对含噪信号的连续Morlet小波变换结果的提纯，只要选择前面合适的奇异值进行SVD重构，大部分噪声奇异值的信息会被抛弃，因而可在很大程度上消除噪声对连续Morlet小波变换结果的影响。

The linear correlation of row vectors of the continuous Morlet wavelet transform matrix is studied, this correlation make the result of continuous Morlet wavelet transform be of great redundancy, and singular value decomposition(SVD) is proposed to compress this redundancy. Theoretical analysis shows that the continuous Morlet wavelet transform matrix can be compressed into a few non-zero singular values and the corresponding orthogonal singular vectors by SVD technology. The ratio of the data size before and after compression is analyzed, and it is shown that the larger the matrix dimension, the better the compression effect. The distribution characteristics of singular values of the continuous Morlet wavelet transform results of the deterministic signal and the noise is studied, and it is found that the number of the effective singular values of the deterministic signal is determined by the number of frequencies in this signal, and the other singular values after the effective ones will soon drop to zero, while the singular values of noise is changed evenly and its falling speed is slow. This difference between the singular values of the deterministic signal and the noise is utilized, the purification of the continuous Morlet wavelet transform result of noisy signal can be realized, if the appropriate front singular values are chosen for SVD reconstruction, then the information of most noise singular values is discarded, thus the influence of noise on continuous Morlet wavelet transform is erased to a great extent.