t-Distributed Stochastic Neighborhood Embedding
An S4 Class for t-SNE.
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.
funA function that does the embedding and returns a dimRedResult object.
stdparsThe standard parameters for the function.
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.
t-SNE can take the following parameters:
A distance function, defaults to euclidean distances
The perplexity parameter, roughly equivalent to neighborhood size.
Approximation for the nearest neighbour search, large values are more inaccurate.
The number of embedding dimensions.
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.
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.
Other dimensionality reduction methods: AutoEncoder-class,
DRR-class,
DiffusionMaps-class,
DrL-class, FastICA-class,
FruchtermanReingold-class,
HLLE-class, Isomap-class,
KamadaKawai-class, LLE-class,
MDS-class, NNMF-class,
PCA-class, PCA_L1-class,
UMAP-class,
dimRedMethod-class,
dimRedMethodList, kPCA-class,
nMDS-class
## Not run:
dat <- loadDataSet("3D S Curve", n = 300)
## using the S4 class directly:
tsne <- tSNE()
emb <- tsne@fun(dat, tsne@stdpars)
## using embed()
emb2 <- embed(dat, "tSNE", perplexity = 80)
plot(emb, type = "2vars")
plot(emb2, type = "2vars")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.