# Iterative thresholding sparse SVD

### Description

The function computes sparse SVD by iterative thresholding algorithm with an initializtion as one of the inputs

### Usage

1 2 3 |

### Arguments

`x` |
Input matrix, for which one would like to get a sparse SVD. |

`method` |
If method = "theory", then a theoretical procedure is adopted which is based on normal assumption on the noise. If method = "method", then the function bypass the normal assumption by some robust statistics. These two choices typically give similar solutions, but "theory" is much faster. |

`u.old` |
A matrix that contains initial left singular vectors as the columns of the matrix. |

`v.old` |
A matrix that contains initial right singular vectors as the columns of the matrix. |

`gamma.u` |
When the method="theory", gamma.u=sqrt(2) corresponds to the sqrt(2 log(p)), which is the largest magnitude of p iid standard normals. If gamma.u is manually set to be smaller or larger than sqrt2, the left singular vectors will be denser or sparser respectively. |

`gamma.v` |
When the method="theory", gamma.u=sqrt(2) corresponds to the sqrt(2 log(p)), which is the largest magnitude of p iid standard normals. If gamma.u is manually set to be smaller or larger than sqrt2, the right singular vectors will be denser or sparser respectively. |

`dothres` |
Dothres has two choices, either "hard" or "soft", which means hard-thresholding or soft-thresholding |

`r` |
A scaler, the number of components, i.e., the number of singular vectors to be computed. |

`tol` |
The tolerance level that determines when the algorithm stops. |

`n.iter` |
Maximum number of iterations allowed. |

`n.boot` |
Number of bootstrap to estimate the threshold level when method = "method" |

`sigma` |
Sigma is a scaler for the noise level. The user can set it to be NA, and the function will estimate it automatically. |

`non.orth` |
If non.orth=TRUE, then the last iteration of the algorithm will not involve orthoganolization, which should produce sparse solutions. |

### Value

`u` |
A matrix containing left singular vectors |

`v` |
A matrix containing right singular vectors |

`d` |
A vector containing singular values |

`niter` |
Number of iterations for the algorithm to converge |

`sigma.hat` |
An estimate of the noise level |

`dist.u` |
The distance between the left singular vectors of the last two iterations, can be used to see whether the algorithm indeed converges. |

`dist.v` |
The distance between the right singular vectors of the last two iterations, can be used to see whether the algorithm indeed converges. |

### Author(s)

Dan Yang

### References

A Sparse SVD Method for High-dimensional Data

### Examples

1 2 3 | ```
ans.initial <- ssvd.initial(matrix(rnorm(2^15),2^7,2^8), method = "method")
ans.iter <- ssvd.iter.thresh(matrix(rnorm(2^15),2^7,2^8),
u.old=ans.initial$u, v.old= ans.initial$v, method = "method")
``` |

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