An S4 Class for t-SNE.
Details
t-SNE is a method that uses Kullback-Leibler divergence between the distance matrices in high and low-dimensional space to embed the data. The method is very well suited to visualize complex structures in low dimensions.
Slots
funA function that does the embedding and returns a dimRedResult object.
stdparsThe standard parameters for the function.
General usage
Dimensionality reduction methods are S4 Classes that either be used
directly, in which case they have to be initialized and a full
list with parameters has to be handed to the @fun()
slot, or the method name be passed to the embed function and
parameters can be given to the ..., in which case
missing parameters will be replaced by the ones in the
@stdpars.
Parameters
t-SNE can take the following parameters:
- d
A distance function, defaults to euclidean distances
- perplexity
The perplexity parameter, roughly equivalent to neighborhood size.
- theta
Approximation for the nearest neighbour search, large values are more inaccurate.
- ndim
The number of embedding dimensions.
Implementation
Wraps around Rtsne, which is very well
documented. Setting theta = 0 does a normal t-SNE, larger
values for theta < 1 use the Barnes-Hut algorithm which
scales much nicer with data size. Larger values for perplexity take
larger neighborhoods into account.
References
Maaten, L. van der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research 15, 3221-3245.
van der Maaten, L., Hinton, G., 2008. Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579-2605.
See also
Other dimensionality reduction methods:
DRR-class,
DiffusionMaps-class,
DrL-class,
FastICA-class,
FruchtermanReingold-class,
HLLE-class,
Isomap-class,
KamadaKawai-class,
MDS-class,
NNMF-class,
PCA-class,
PCA_L1-class,
UMAP-class,
dimRedMethod-class,
dimRedMethodList(),
kPCA-class,
nMDS-class
Examples
if (FALSE) { # \dontrun{
dat <- loadDataSet("3D S Curve", n = 300)
emb <- embed(dat, "tSNE", perplexity = 80)
plot(emb, type = "2vars")
} # }