SSLR: LaplacianSVMSSLR – R documentation

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LaplacianSVMSSLR

General Interface for LaplacianSVM model

Description

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).

Usage

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)
m <- LaplacianSVMSSLR(kernel=kernlab::vanilladot()) %>%
  fit(Class ~ ., data = train)

#Accuracy
predict(m,test) %>%
  bind_cols(test) %>%
  metrics(truth = "Class", estimate = .pred_class)

SSLR

Semi-Supervised Classification, Regression and Clustering Methods

v0.9.3.1

GPL-3

Authors

Francisco Jesús Palomares Alabarce [aut, cre] (<https://orcid.org/0000-0002-0499-7034>), José Manuel Benítez [ctb] (<https://orcid.org/0000-0002-2346-0793>), Isaac Triguero [ctb] (<https://orcid.org/0000-0002-0150-0651>), Christoph Bergmeir [ctb] (<https://orcid.org/0000-0002-3665-9021>), Mabel González [ctb] (<https://orcid.org/0000-0003-0152-444X>)

Initial release