R Deep Learning Cookbook

Setting up principal component analysis

1. Before getting into non-linear manifolds, let's analyze principal component analysis on the occupancy data:

# Setting-up principal component analysis pca_obj <- prcomp(occupancy_train$data,                 center = TRUE,                 scale. = TRUE)                 scale. = TRUE)

2. The preceding function will transform the data into six orthogonal directions specified as linear combinations of features. The variance explained by each dimension can be viewed using the following script:

plot(pca_obj, type = "l")

3. The preceding command will plot the variance across principal components, as shown in the following figure:

4. For the occupancy dataset, ...