RSSL: LaplacianKernelLeastSquaresClassifier – R documentation

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LaplacianKernelLeastSquaresClassifier

Laplacian Regularized Least Squares Classifier

Description

Implements manifold regularization through the graph Laplacian as proposed by Belkin et al. 2006. As an adjacency matrix, we use the k nearest neighbour graph based on a chosen distance (default: euclidean).

Usage

LaplacianKernelLeastSquaresClassifier(X, y, X_u, lambda = 0, gamma = 0,
  kernel = kernlab::vanilladot(), adjacency_distance = "euclidean",
  adjacency_k = 6, x_center = TRUE, scale = TRUE, y_scale = TRUE,
  normalized_laplacian = FALSE)

Arguments

`X`	matrix; Design matrix for labeled data
`y`	factor or integer vector; Label vector
`X_u`	matrix; Design matrix for unlabeled data
`lambda`	numeric; L2 regularization parameter
`gamma`	numeric; Weight of the unlabeled data
`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.
`x_center`	logical; Should the features be centered?
`scale`	logical; Should the features be normalized? (default: FALSE)
`y_scale`	logical; whether the target vector should be centered
`normalized_laplacian`	logical; If TRUE use the normalized Laplacian, otherwise, the Laplacian is used

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(RSSL)
library(ggplot2)
library(dplyr)

## Example 1: Half moons

# Generate a dataset
set.seed(2)
df_orig <- generateCrescentMoon(100,sigma = 0.3) 
df <- df_orig %>% 
  add_missinglabels_mar(Class~.,0.98)

lambda <- 0.01
gamma <- 10000
rbf_param <- 0.125

# Train classifiers
## Not run: 
class_sup <- KernelLeastSquaresClassifier(
                Class~.,df,
                kernel=kernlab::rbfdot(rbf_param),
                lambda=lambda,scale=FALSE)

class_lap <- LaplacianKernelLeastSquaresClassifier(
                    Class~.,df,
                    kernel=kernlab::rbfdot(rbf_param),
                    lambda=lambda,gamma=gamma,
                    normalized_laplacian = TRUE,
                    scale=FALSE)

classifiers <- list("Lap"=class_lap,"Sup"=class_sup)

# Plot classifiers (can take a couple of seconds)

df %>% 
  ggplot(aes(x=X1,y=X2,color=Class)) +
  geom_point() +
  coord_equal() +
  stat_classifier(aes(linetype=..classifier..),
                  classifiers = classifiers ,
                  color="black")


# Calculate the loss
lapply(classifiers,function(c) mean(loss(c,df_orig)))

## End(Not run)

## Example 2: Two circles
set.seed(1)
df_orig <- generateTwoCircles(1000,noise=0.05)
df <- df_orig %>% 
  add_missinglabels_mar(Class~.,0.994)

lambda <- 10e-12
gamma <- 100
rbf_param <- 0.1

# Train classifiers
## Not run: 
class_sup <- KernelLeastSquaresClassifier(
  Class~.,df,
  kernel=kernlab::rbfdot(rbf_param),
  lambda=lambda,scale=TRUE)

class_lap <- LaplacianKernelLeastSquaresClassifier(
  Class~.,df,
  kernel=kernlab::rbfdot(rbf_param),
  adjacency_k = 30,
  lambda=lambda,gamma=gamma,
  normalized_laplacian = TRUE,
  scale=TRUE)

classifiers <- list("Lap"=class_lap,"Sup"=class_sup)

# Plot classifiers (Can take a couple of seconds)
df %>% 
  ggplot(aes(x=X1,y=X2,color=Class,size=Class)) +
  scale_size_manual(values=c("1"=3,"2"=3),na.value=1) +
  geom_point() +
  coord_equal() +
  stat_classifier(aes(linetype=..classifier..),
                  classifiers = classifiers ,
                  color="black",size=1)

## End(Not run)

RSSL

Implementations of Semi-Supervised Learning Approaches for Classification

v0.9.3

GPL (>= 2)

Authors

Jesse Krijthe [aut, cre]

Initial release