Description Usage Arguments Value Author(s) References Examples

Robust (quadratic) discriminant analysis implements a discriminant analysis method which is robust to label noise. This function implements the method described in Lawrence and Scholkopf (2003, ISBN:1-55860-778-1).

1 |

`X` |
a data frame containing the learning observations. |

`lbl` |
the class labels of the learning observations. |

`Y` |
a data frame containing the new observations to classify. |

`maxit` |
the maximum number of iterations. |

`disp` |
logical, if |

`...` |
additional arguments to provide to subfunctions. |

A list is returned with the following elements:

`nu` |
the estimated class proportions. |

`mu` |
the estimated class means. |

`S` |
the estimated covariance matrices. |

`gamma` |
the estimated purity level of the labels. |

`Ti` |
the posterior probabilties of the labels knowing the observed labels for the learning observations. |

`Pi` |
the class posterior probabilities of the observations to classify. |

`cls` |
the class assignments of the observations to classify. |

`ll` |
the log-likelihood value. |

C. Bouveyron

Lawrence, N., and Scholkopf, B., Estimating a kernel Fisher discriminant in the presence of label noise, Pages 306–313 of: Proceedings of the Eighteenth International Conference on Machine Learning. ICML’01. San Francisco, CA, USA, 2001 (ISBN:1-55860-778-1).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
n = 50
m1 = c(0,0); m2 = 1.5*c(1,-1)
S1 = 0.1*diag(2); S2 = 0.25 * diag(2)
X = rbind(mvrnorm(n,m1,S1),mvrnorm(2*n,m2,S2))
cls = rep(1:2,c(n,2*n))
# Label perturbation
ind = rbinom(3*n,1,0.4); lb = cls
lb[ind==1 & cls==1] = 2
lb[ind==1 & cls==2] = 1
# Classification with RQDA
res = rqda(X,lb,X)
table(cls,res$cls)
``` |

```
Loading required package: mclust
Package 'mclust' version 5.4.7
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: Rmixmod
Loading required package: Rcpp
Rmixmod v. 2.1.5 / URI: www.mixmod.org
Loading required package: MASS
Loading required package: mvtnorm
Attaching package: ‘mvtnorm’
The following object is masked from ‘package:mclust’:
dmvnorm
..................................................
cls 1 2
1 49 1
2 0 100
```

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