SSLR (version 0.9.3.3)

GRFClassifierSSLR: General Interface for GRFClassifier (Label propagation using Gaussian Random Fields and Harmonic) model

Description

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.

Usage

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

Zhu, X., Ghahramani, Z. & Lafferty, J., 2003 Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning. pp. 912-919.

Examples

# NOT RUN {
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)

#Accesing model from RSSL
model <- m$model

#Predictions of unlabeled
preds_unlabeled <- m %>% predictions()
print(preds_unlabeled)

preds_unlabeled <- m %>% predictions(type = "raw")
print(preds_unlabeled)

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