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Calculates the Singular Value Decomposition of the input matrix into U×S×V', such that U and V are orthogonal and S is diagonal. Returns an image with bands named 'U', 'S' and 'V'.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\u003cp\u003eThis operation calculates the Singular Value Decomposition (SVD) of a 2-D matrix image, breaking it down into three components: U, S, and V'.\u003c/p\u003e\n"],["\u003cp\u003eThe result is a new image containing bands named 'U', 'S', and 'V', representing the orthogonal matrices U and V, and the diagonal matrix S, respectively.\u003c/p\u003e\n"],["\u003cp\u003eSVD is a factorization method used in linear algebra to decompose a matrix into its constituent parts.\u003c/p\u003e\n"]]],[],null,["# ee.Image.matrixSingularValueDecomposition\n\nCalculates the Singular Value Decomposition of the input matrix into U×S×V', such that U and V are orthogonal and S is diagonal. Returns an image with bands named 'U', 'S' and 'V'.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------------------|---------|\n| Image.matrixSingularValueDecomposition`()` | Image |\n\n| Argument | Type | Details |\n|---------------|-------|-----------------------------------------|\n| this: `image` | Image | Image of 2-D matrices to be decomposed. |"]]
