Embed new samples with Manifold Sculpting

Manifold Sculpting has no explicit mapping from the data to the embedding. A new sample is embedded from its neighbors nearest training samples: it starts at the combination of their embeddings given by its locally linear reconstruction weights (as in locally linear embedding), and is then moved to minimize the Manifold Sculpting error, the discrepancy between its original and embedded distances and angles to its neighbors, whose embeddings are held fixed. A new sample that is identical to a training sample takes its embedding, so the training data are mapped to manifold_sculpting()'s embedding.

Usage

# S3 method for class 'manifold_sculpting'
predict(object, new_data, refine = TRUE, ...)

Arguments

object

A manifold_sculpting() fit.

new_data

A numeric matrix or data frame with the variables used to fit object. Columns are matched by name when object was fitted with column names, and by position otherwise.

refine

If FALSE, the samples stay at their locally linear reconstruction, without the error minimization.

...

Not used.

Value

A matrix with one row per sample of new_data and object$ndim columns.

Examples

set.seed(1)
roll <- sim_swiss_roll(1000)
train <- roll[1:800, ]
test <- roll[801:1000, ]
fit <- manifold_sculpting(train[c("x", "y", "z")], nstart = 3)
head(predict(fit, test))
#>            MS1       MS2
#> [1,] -15.65776 13.589965
#> [2,] -20.51788  4.960831
#> [3,] -15.11771 14.832491
#> [4,] -16.49914 12.496426
#> [5,] -19.30644  7.569465
#> [6,] -17.01433 11.848336