Description: PCA (principal component analysis), or principal component analysis, is one of the most widely used data dimensionality reduction algorithms. The main idea of PCA is to map the n-dimensional feature to the k-dimensional feature, which is a new orthogonal feature, also known as the main component. It is a k-dimensional feature reconstructed on the basis of the original n-dimensional feature.
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|PCA求解输出投影矩阵\PCA求解输出投影矩阵.ipynb|| 22611 || 2020-01-08
|PCA求解输出投影矩阵\sample.txt|| 19470 || 2020-01-08
|PCA求解输出投影矩阵\练习.jpg|| 36507 || 2020-01-08
|PCA求解输出投影矩阵|| 0 || 2020-01-08|