Description Usage Arguments Details Value Author(s) See Also Examples
CRDA approach performs simultaneous feature selection and classification of high-dimensional data.
The function crda
classifies each column of the test data set Xt (p x N) into one of
the G classes. Test data set Xt and training data set X must have the
same number of rows (features or variables). Vector y is a
class variable of training data. Its unique values define classes; each
element defines the class to which the corresponding column of X belongs.
The input y is a numeric vector with integer elements ranging from
1,2,..,G, where G is the number of classes. Note that y must have the
same number of rows as there are columns in X. The output yhat indicates
the class to which each column of Xt has been assigned. Also yhat is Nx1
vector of integers ranging from 1,2,...,G.
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. |
Xt |
Test dataset, a pxnt matrix with nt-samples each of p-features. |
q |
scalar (>=1) or string 'var' or 'inf' or 'cv'. If q is a real scalar, then it must be >= 1 and it denotes the L_q-norm to be used in the hard thresholding operator H_K(B,phi) in the CRDA method. If q is equal to a string 'inf' (or 'var') then L_infty norm will be used (or sample variance) will be used as the hard-thresholding selector function. If q is equal to a string 'cv', then CV is used to select the best hard-thresholding selector function among the L_1-, L_2-, L_inf-norm and the sample variance. |
prior |
Type of prior class probabilities, either 'uniform' (default) or 'estimated'. |
kgrid |
A grid of candidate values for joint-sparsity level. |
nK |
Number of candidate values in the grid of joint-sparsity level. |
nfolds |
Number of folds for cross-validation scheme. |
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. |
B |
Coefficient matrix before feature selection. |
M |
Group-means matrix. |
Ind |
Indices of the length of the row vectors of B organized in descending order, where the length is determined by optional argument 'q'. |
K |
Joint-sparsity level. |
yhat |
Predicted labels for test dataset |
muX |
Optional: Grand-mean, i.e., row (feature) wise mean of training dataset. |
Muhammad Naveed Tabassum and Esa Ollila, 2019.
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