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稀疏PCA的优化解算法，较新的pca算法，供大家学习交流！-Optimal Solutions for Sparse Principal Component Analysis
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实现Focuss 系统的算法程序，可用于稀疏成分分析。-Algorithm to achieve Focuss system procedures can be used for sparse component analysis.
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稀疏主成分分析算法，用于特征提取，维数约简。-Sparse Principle Component Analysis for feature extraction
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gmcalab 快速广义的形态分量分析算法，用于图像修复，稀疏分解、降噪等，用途广泛。从mca主页下载的-gmcalab fast generalized morphological component analysis algorithm for image restoration, sparse decomposition, noise reduction, and versatile. Mca downloaded from the home page
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1一种剪切波域的稀疏分量分析方法.pdf-1.A sparse component analysis of shear wave field
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本程序实现稀疏主成分分析，相关方法在 H. Zou, T. Hastie, and R. Tibshirani的Sparse principal component analysis中有详细介绍(For SPCA, the method introduced in "Sparse principal component analysis" by H. Zou, T. Hastie, and R. Tibshirani)
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