reduce_tsne: Reduce Dimensionality with t-SNE

In laresbernardo/lares: Analytics & Machine Learning Sidekick

Reduce Dimensionality with t-SNE

Description

t-SNE takes high-dimensional data and reduces it to a low-dimensional graph (1-3 dimensions). Unlike PCA, t-SNE can reduce dimensions with non-linear relationships. PCA attempts to draw the best fitting line through the distribution. T-SNE calculates a similarity measure based on the distance between points instead of trying to maximize variance.

Usage

reduce_tsne(df, n = 2, ignore = NULL, quiet = FALSE, plot = TRUE, ...)

Arguments

df	Dataframe
n	Integer. Number of dimensions to reduce to.
ignore	Character vector. Names of columns to ignore.
quiet	Boolean. Keep quiet? If not, print messages.
plot	Boolean. Create plots?
...	Additional parameters passed to Rtsne::Rtsne

Value

List with reduced dataframe and possible plots.

See Also

Other Dimensionality: reduce_pca()

Other Clusters: clusterKmeans(), clusterOptimalK(), clusterVisualK(), reduce_pca()

Examples

## Not run: 
data("iris")
df <- subset(iris, select = c(-Species))
df$id <- seq_len(nrow(df))
reduce_tsne(df, ignore = "id", max_iter = 800, perplexity = 20)

## End(Not run)

laresbernardo/lares documentation built on Oct. 23, 2024, 12:05 p.m.