LaplacianSVMSSLR: General Interface for LaplacianSVM model In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

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

 1 2 3 4 5 6 7 8 9 10 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

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 library(tidyverse) library(caret) library(tidymodels) library(SSLR) data(breast) set.seed(1) train.index <- createDataPartition(breast\$Class, p = .7, list = FALSE) train <- train.index,] test <- -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) #Accesing model from RSSL model <- m\$model #Accuracy predict(m,test) %>% bind_cols(test) %>% metrics(truth = "Class", estimate = .pred_class)

SSLR documentation built on July 22, 2021, 9:08 a.m.