| Type: | Package |
| Title: | Robust Self-Representation Sparse Reconstruction and Manifold Regularization |
| Version: | 0.1.0 |
| Description: | Feature selection and clustering classification under the presence of multivariate outliers in high-dimensional unlabeled data. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 8.0.0 |
| Imports: | robustbase, stats |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2026-05-23 07:09:27 UTC; cvalley |
| Author: | Abdul Wahid [aut, cre] |
| Maintainer: | Abdul Wahid <ab_wahid1996@yahoo.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-05-29 09:50:07 UTC |
Robust Self-Representation Sparse Reconstruction and Manifold Regularization
Description
Performs robust sparse self-representation with manifold regularization.
Usage
RSSRMR(x, Wt, L, alpha = 1, beta = 1, epsilon = 0.001, maxites = 50)
Arguments
x |
Numeric data matrix. |
Wt |
Weight matrix. |
L |
Laplacian matrix. |
alpha |
Regularization parameter. |
beta |
Graph regularization parameter. |
epsilon |
Convergence threshold. |
maxites |
Maximum number of iterations. |
Value
A list containing:
- Optimum.A
Coefficient matrix
- Optimum.G
Diagonal weight matrix
Examples
set.seed(6542)
cluster1 <- matrix(
rnorm(12 * 5, mean = 2, sd = 0.5),
nrow = 12
)
cluster2 <- matrix(
rnorm(13 * 5, mean = 7, sd = 0.5),
nrow = 13
)
X <- rbind(cluster1, cluster2)
wd <- diag(runif(25))
lp <- diag(runif(25))
fit <- RSSRMR(
x = X,
Wt = wd,
L = lp
)
fit$Optimum.G
Robust Distance Weights
Description
Computes robust observation weights.
Usage
dweight(x, q = 0.9)
Arguments
x |
Numeric data matrix. |
q |
Quantile threshold. |
Value
Numeric vector of weights.
Examples
set.seed(123)
x <- matrix(rnorm(50), nrow = 10)
dweight(x)