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  "Title": "Matrix Completion via Iterative Soft-Thresholded SVD",
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  "Description": "Iterative methods for matrix completion that use\nnuclear-norm regularization. There are two main approaches.The\none approach uses iterative soft-thresholded svds to impute the\nmissing values. The second approach uses alternating least\nsquares. Both have an 'EM' flavor, in that at each iteration\nthe matrix is completed with the current estimate. For large\nmatrices there is a special sparse-matrix class named\n\"Incomplete\" that efficiently handles all computations. The\npackage includes procedures for centering and scaling rows,\ncolumns or both, and for computing low-rank SVDs on large\nsparse centered matrices (i.e. principal components).",
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    "softImpute.x.Incomplete",
    "softImpute.x.matrix",
    "splr",
    "Ssimpute.als",
    "Ssimpute.svd",
    "Ssvd.als",
    "svd.als"
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    {
      "page": "biScale",
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      ]
    },
    {
      "page": "clean.warm.start",
      "title": "rdname softImpute-internal",
      "topics": [
        "clean.warm.start"
      ]
    },
    {
      "page": "deBias",
      "title": "Recompute the '$d' component of a '\"softImpute\"' object through regression.",
      "topics": [
        "deBias"
      ]
    },
    {
      "page": "impute",
      "title": "make predictions from an svd object",
      "topics": [
        "complete",
        "complete,Incomplete-method",
        "complete,matrix-method",
        "impute"
      ]
    },
    {
      "page": "Incomplete",
      "title": "create a matrix of class 'Incomplete'",
      "topics": [
        "coerce,matrix-method",
        "Incomplete"
      ]
    },
    {
      "page": "Incomplete-class",
      "title": "Class '\"Incomplete\"'",
      "topics": [
        "as.matrix,Incomplete-method",
        "coerce,matrix,Incomplete-method",
        "coerce,sparseMatrix,Incomplete-method",
        "Incomplete-class"
      ]
    },
    {
      "page": "lambda0",
      "title": "compute the smallest value for 'lambda' such that 'softImpute(x,lambda)' returns the zero solution.",
      "topics": [
        "lambda0",
        "lambda0,Incomplete-method",
        "lambda0,sparseMatrix-method",
        "lambda0,SparseplusLowRank-method"
      ]
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      ]
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      "title": "rdname softImpute-internal",
      "topics": [
        "simpute.svd"
      ]
    },
    {
      "page": "softImpute",
      "title": "impute missing values for a matrix via nuclear-norm regularization.",
      "topics": [
        "softImpute"
      ]
    },
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      "page": "softImpute.x.Incomplete",
      "title": "rdname softImpute-internal",
      "topics": [
        "softImpute.x.Incomplete"
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      "title": "Internal softImpute functions",
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      "page": "SparseplusLowRank-class",
      "title": "Class '\"SparseplusLowRank\"'",
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        "%*%,ANY,SparseplusLowRank-method",
        "%*%,Matrix,SparseplusLowRank-method",
        "%*%,SparseplusLowRank,ANY-method",
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