This function performs dimensionality reduction using various methods including PCA, t-SNE, and UMAP. For efficiency and consistency, PCA preprocessing is always performed first, and t-SNE/UMAP use the PCA results as input. This follows best practices for high-dimensional data analysis.
Usage
reduce_dimensions(
data_matrix,
method = "PCA",
n_components = 2,
pca_dims = 50,
tsne_perplexity = 30,
tsne_max_iter = 1000,
umap_neighbors = 15,
umap_min_dist = 0.1,
umap_metric = "cosine",
seed = 123,
verbose = TRUE
)Arguments
- data_matrix
A numeric matrix where rows represent documents and columns represent features.
- method
The dimensionality reduction method. Options: "PCA", "t-SNE", "UMAP".
- n_components
The number of components/dimensions to reduce to (default: 2).
- pca_dims
The number of dimensions for PCA preprocessing (default: 50).
- tsne_perplexity
The perplexity parameter for t-SNE (default: 30).
- tsne_max_iter
The maximum number of iterations for t-SNE (default: 1000).
- umap_neighbors
The number of neighbors for UMAP (default: 15).
- umap_min_dist
The minimum distance for UMAP (default: 0.1).
- umap_metric
The metric for UMAP (default: "cosine").
- seed
Random seed for reproducibility (default: 123).
- verbose
Logical, if TRUE, prints progress messages.
Examples
if (interactive()) {
mydata <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_cols(
mydata,
listed_vars = c("title", "keyword", "abstract")
)
tokens <- TextAnalysisR::prep_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
data_matrix <- as.matrix(dfm_object)
pca_result <- TextAnalysisR::reduce_dimensions(
data_matrix,
method = "PCA"
)
print(pca_result)
tsne_result <- TextAnalysisR::reduce_dimensions(
data_matrix,
method = "t-SNE"
)
print(tsne_result)
umap_result <- TextAnalysisR::reduce_dimensions(
data_matrix,
method = "UMAP"
)
print(umap_result)
}