T-distributed Stochastic Neighbor Embedding res = tSNE(Data, KNN=30,OutputDimension=2)
tSNE(DataOrDists,k,OutputDimension=2,method="euclidean",Whitening=TRUE,InitialDimensions=NULL, Iterations=1000,PlotIt=FALSE,Cls)
array of data: n cases in rows, d variables in columns, matrix is not symmetric or distance matrix, in this case matrix has to be symmetric
number of k nearest neighbors=number of effective nearest neighbors("perplexity") Important parameter, if not given Settings of package t-SNE will be used
Number of dimensions in the Outputspace, default=2
method specified by distance string: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary'
A boolean value indicating whether the matrix data should be whitened
The number of dimensions to use in reduction method.
maximum number of iterations to perform.
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data.
ProjectedPoints[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projection