Graphical Singular Value Decomposition SVD

Enterprise 2023-04-08 21:13:00 views: null

- - singular value decomposition

singular value decomposition

Purpose

Decompose an arbitrary matrix M into the product form of three matrices
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linear transformation

Stretch:
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Rotation:

Left multiply an orthogonal matrix
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Physical meaning of decomposition

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generalizes to matrices of any size

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The singular value decreases sequentially from top to bottom, and the part with small singular value can be removed for data compression.

The effect after compression:
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Compressed reconstruction (Using U, V and Σ with some values removed)

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Calculation of SVD decomposition

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Introduce the calculation of eigenvalues
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Calculation process

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Origin blog.csdn.net/gary101818/article/details/123665697

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