General Interface for GRFClassifier (Transductive SVM classifier using the convex concave procedure) model

model from RSSL package Implements the approach proposed in Zhu et al. (2003) to label propagation over an affinity graph. Note, as in the original paper, we consider the transductive scenario, so the implementation does not generalize to out of sample predictions. The approach minimizes the squared difference in labels assigned to different objects, where the contribution of each difference to the loss is weighted by the affinity between the objects. The default in this implementation is to use a knn adjacency matrix based on euclidean distance to determine this weight. Setting adjacency="heat" will use an RBF kernel over euclidean distances between objects to determine the weights.

GRFClassifierSSLR(
  adjacency = "nn",
  adjacency_distance = "euclidean",
  adjacency_k = 6,
  adjacency_sigma = 0.1,
  class_mass_normalization = TRUE,
  scale = FALSE,
  x_center = FALSE
)

Arguments

adjacency	character; "nn" for nearest neighbour graph or "heat" for radial basis adjacency matrix
adjacency_distance	character; distance metric for nearest neighbour adjacency matrix
adjacency_k	integer; number of neighbours for the nearest neighbour adjacency matrix
adjacency_sigma	double; width of the rbf adjacency matrix
class_mass_normalization	logical; Should the Class Mass Normalization heuristic be applied? (default: TRUE)
scale	logical; Should the features be normalized? (default: FALSE)
x_center	logical; Should the features be centered?

References

Collobert, R. et al., 2006. Large scale transductive SVMs. Journal of Machine Learning Research, 7, pp.1687-1712.

Examples

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

data(wine)


cls <- which(colnames(wine) == "Wine")

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


m <- GRFClassifierSSLR() %>% fit(Wine ~ ., data = wine)

#Predictions of unlabeled

preds_unlabeled <- m %>% predictions()
print(preds_unlabeled)
#> # A tibble: 141 x 1
#>    .pred_class
#>    <fct>      
#>  1 1          
#>  2 1          
#>  3 1          
#>  4 1          
#>  5 1          
#>  6 1          
#>  7 1          
#>  8 1          
#>  9 1          
#> 10 1          
#> # ... with 131 more rows

preds_unlabeled <- m %>% predictions(type = "raw")
print(preds_unlabeled)
#>   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1
#>  [38] 1 1 1 1 1 1 1 1 1 1 3 3 2 3 2 3 2 2 2 1 2 2 1 1 2 2 3 1 2 2 3 3 3 2 2 2 3
#>  [75] 3 2 2 1 3 3 2 3 2 2 3 2 2 2 3 2 3 2 2 2 2 3 2 2 2 2 2 2 3 3 3 2 3 3 3 3 3
#> [112] 3 1 2 1 1 2 3 3 2 3 2 2 3 2 1 3 3 3 3 2 3 3 1 3 2 3 1 1 1 2
#> Levels: 1 2 3

#Total
y_total <- wine[,cls]
y_total[-labeled.index] <- preds_unlabeled