General Interface for LaplacianSVM model

model from RSSL package Manifold regularization applied to the support vector machine as proposed in Belkin et al. (2006). As an adjacency matrix, we use the k nearest neighbour graph based on a chosen distance (default: euclidean).

LaplacianSVMSSLR(
  lambda = 1,
  gamma = 1,
  scale = TRUE,
  kernel = kernlab::vanilladot(),
  adjacency_distance = "euclidean",
  adjacency_k = 6,
  normalized_laplacian = FALSE,
  eps = 1e-09
)

Arguments

lambda	numeric; L2 regularization parameter
gamma	numeric; Weight of the unlabeled data
scale	logical; Should the features be normalized? (default: FALSE)
kernel	kernlab::kernel to use
adjacency_distance	character; distance metric used to construct adjacency graph from the dist function. Default: "euclidean"
adjacency_k	integer; Number of of neighbours used to construct adjacency graph.
normalized_laplacian	logical; If TRUE use the normalized Laplacian, otherwise, the Laplacian is used
eps	numeric; Small value to ensure positive definiteness of the matrix in the QP formulation

References

Belkin, M., Niyogi, P. & Sindhwani, V., 2006. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, pp.2399-2434.

Examples

library(tidyverse)
library(caret)
library(tidymodels)
library(SSLR)

data(breast)

set.seed(1)
train.index <- createDataPartition(breast$Class, p = .7, list = FALSE)
train <- breast[ train.index,]
test  <- breast[-train.index,]

cls <- which(colnames(breast) == "Class")

#% LABELED
labeled.index <- createDataPartition(breast$Class, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA

library(kernlab)
#> 
#> Attaching package: 'kernlab'
#> The following object is masked from 'package:scales':
#> 
#>     alpha
#> The following object is masked from 'package:purrr':
#> 
#>     cross
#> The following object is masked from 'package:ggplot2':
#> 
#>     alpha
m <- LaplacianSVMSSLR(kernel=kernlab::vanilladot()) %>%
  fit(Class ~ ., data = train)

#Accuracy
predict(m,test) %>%
  bind_cols(test) %>%
  metrics(truth = "Class", estimate = .pred_class)
#> # A tibble: 2 x 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 accuracy binary         0.706
#> 2 kap      binary         0