Description Usage Arguments Details Value Author(s) See Also Examples

The function `crda`

implements Compressive Regularized Discriminant Analysis (CRDA) approach and performs simultaneous feature selection and classification of high-dimensional data.
CRDA approach aims to address three facets of high-dimensional classification: namely, accuracy, computational complexity, and interpretability.

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`X` |
Training dataset, a pxn matrix with n-samples each having p-features. |

`y` |
Labels for training dataset, an nx1 vector of whole numbers. |

`q` |
Type of Lq,1 norm, default is Linf-norm. |

`al` |
Regularization parameter. |

`K` |
Joint-sparsity level. |

`Xt` |
Test dataset, a pxnt matrix with nt-samples each of p-features. |

`prior` |
Type of prior class probabilities, either 'uniform' (default) or 'estimated'. |

`centerX` |
Flag for grand-mean centering of test dataset using grand-mean of training dataset. |

crda

An object `obj`

of class `crda`

with the following attributes:

`funCall` |
The call to the |

`prior` |
Prior class probabilities. |

`varSelRate` |
Feature selection rate (FSR). |

`selVarPos` |
Position (i.e., index) of selected features. |

`coefMat` |
Coefficient matrix before feature selection. |

`shrunkenCoefMat` |
Shrunken (rowsparse) coefficient matrix. |

`const` |
The constant part of discriminant function for CRDA method. |

`predTrainLabels` |
Predicted labels for training dataset. |

`predTestLabels` |
Optional: Predicted labels for test dataset, if it is available. |

`regparam` |
Optional: The value of regularization parameter. |

`muX` |
Optional: Grand-mean, i.e., row (feature) wise mean of training dataset. |

Muhammad Naveed Tabassum and Esa Ollila, 2018

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mntabassm/compressiveRDA documentation built on May 31, 2019, 5:22 p.m.

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