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  "Description": "For a data matrix with m rows and n columns (m>=n), the\npower method is used to compute, simultaneously, the\neigendecomposition of a square symmetric matrix. This result is\nused to obtain the singular value decomposition (SVD) and the\nprincipal component analysis (PCA) results. Compared to the\nclassical SVD method, the first r singular values can be\ncomputed.",
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    {
      "page": "psvd-package",
      "title": "Eigendecomposition, Singular-Values and the Power Method",
      "topics": [
        "psvd-package"
      ]
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        "calcPCA"
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      "title": "Perform singular values decomposition",
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        "calcSVD"
      ]
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        "eigenV"
      ]
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    {
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      "title": "Compute the eigenvectors of a square symmetric matrix",
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        "eigenVc"
      ]
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      "title": "Modified Gram-Schmidt orthogonalization of a matrix",
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      "title": "Modified Gram-Schmidt orthogonalization of a matrix",
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