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奇异值分解(singular value decomposition)的几何解释及其应用.......

xiaoxiong posted @ 2014年12月11日 21:06 in 未分类 , 789 阅读

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Emma 说:
2023年1月25日 17:36

The singular value decomposition (SVD) provides a powerful geometric interpretation of linear transformations. This makes it a particularly useful tool in applications such as data compression, image engagement rings processing, and principal component analysis. By decomposing a matrix into its constituent singular values, SVD offers a way to understand the structure of the data and how it is affected by linear transformations. Through its implementation, SVD can be used to reduce the dimensionality of a data set and extract meaningful insights. It is a powerful technique for understanding and manipulating data in a variety of contexts.


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