qtSNE: qKernel t-Distributed Stochastic Neighbor Embedding

In qkerntool: Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools

Description

Wrapper for the qkernel t-distributed stochastic neighbor embeddingg. qtSNE is a method for constructing a low dimensional embedding of high-dimensional data, distances or similarities.

Usage

## S4 method for signature 'matrix'
qtSNE(x,kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
        initial_config = NULL, no_dims=2, initial_dims=30, perplexity=30, max_iter= 1300,
         min_cost=0, epoch_callback=NULL, epoch=100, na.action = na.omit, ...)
## S4 method for signature 'cndkernmatrix'
qtSNE(x,initial_config = NULL, no_dims=2, initial_dims=30,
        perplexity=30, max_iter = 1000, min_cost=0, epoch_callback=NULL,epoch=100)
## S4 method for signature 'qkernmatrix'
qtSNE(x,initial_config = NULL, no_dims=2, initial_dims=30,
        perplexity=30, max_iter = 1000, min_cost=0, epoch_callback=NULL,epoch=100)

Arguments

x	the matrix of data to be clustered or a kernel Matrix of class qkernmatrix or cndkernmatrix.
kernel	the kernel function used in computing the affinity matrix. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: rbfbase Radial Basis qkernel function "Gaussian" nonlbase Non Linear qkernel function laplbase Laplbase qkernel function ratibase Rational Quadratic qkernel function multbase Multiquadric qkernel function invbase Inverse Multiquadric qkernel function wavbase Wave qkernel function powbase Power qkernel function logbase Log qkernel function caubase Cauchy qkernel function chibase Chi-Square qkernel function studbase Generalized T-Student qkernel function nonlcnd Non Linear cndkernel function polycnd Polynomial cndkernel function rbfcnd Radial Basis cndkernel function "Gaussian" laplcnd Laplacian cndkernel function anocnd ANOVA cndkernel function raticnd Rational Quadratic cndkernel function multcnd Multiquadric cndkernel function invcnd Inverse Multiquadric cndkernel function wavcnd Wave cndkernel function powcnd Power cndkernel function logcnd Log cndkernel function caucnd Cauchy cndkernel function chicnd Chi-Square cndkernel function studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.
qpar	a character string or the list of hyper-parameters (kernel parameters). The default character string list(sigma = 2, q = 0.9) uses a heuristic to determine a suitable value for the width parameter of the RBF kernel. The second option "local" (local scaling) uses a more advanced heuristic and sets a width parameter for every point in the data set. This is particularly useful when the data incorporates multiple scales. A list can also be used containing the parameters to be used with the kernel function. Valid parameters for existing kernels are : sigma for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" the Cauchy qkernel function "caubase" and for the ANOVA cndkernel function "anocnd". alpha for the Non Linear qkernel function "nonlbase",for the Non Linear cndkernel function "nonlcnd",and for the Polynomial cndkernel function "polycnd". c for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase", the Inverse Multiquadric qkernel function "invbase",for the Polynomial cndkernel function "polycnd",for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd". d for qkernel function "powbase" , the Log qkernel function "logbase", the Generalized T-Student qkernel function "studbase", for the Polynomial cndkernel function "polycnd", for the ANOVA cndkernel function "anocnd",for the d cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". theta for the Wave qkernel function "wavbase" and for the Wave cndkernel function "wavcnd". gamma for the Chi-Square qkernel function "chibase",for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". q For all qkernel Function. where length is the length of the strings considered, lambda the decay factor and normalized a logical parameter determining if the kernel evaluations should be normalized. Hyper-parameters for user defined kernels can be passed through the qkpar parameter as well.
initial_config	An intitial configure about x (default: NULL)
no_dims	the dimension of the resulting embedding. (default: 2)
initial_dims	The number of dimensions to use in reduction method. (default: 30)
perplexity	Perplexity parameter
max_iter	Number of iterations (default: 1300)
min_cost	The minimum cost for every object after the final iteration
epoch_callback	A callback function used after each epoch (an epoch here means a set number of iterations)
epoch	The interval of the number of iterations displayed (default: 100)
na.action	the action to perform on NA
...	Other arguments that can be passed to qtSNE

Details

When the initial_config argument is specified, the algorithm will automatically enter the final momentum stage. This stage has less large scale adjustment to the embedding, and is intended for small scale tweaking of positioning. This can greatly speed up the generation of embeddings for various similar X datasets, while also preserving overall embedding orientation.

Value

qtSNE gives out an S4 object which is a LIST with components

dimRed	Matrix containing the new representations for the objects after qtSNE
cndkernf	The kernel function used

Author(s)

Yusen Zhang
[e-mail]

References

Maaten, L. Van Der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research, 15, p.3221-3245.

van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605.

Examples

## Not run: 
#use iris data set
data(iris)
testset <- sample(1:150,20)
train <- as.matrix(iris[,1:4])

colors = rainbow(length(unique(iris$Species)))
names(colors) = unique(iris$Species)
#for matrix
ecb = function(x,y){
  plot(x,t='n');
  text(x,labels=iris$Species, col=colors[iris$Species])
}
kpc2 <- qtSNE(train, kernel = "rbfbase", qpar = list(sigma=1,q=0.8),
              epoch_callback = ecb, perplexity=10, max_iter = 500)


## End(Not run)

qkerntool documentation built on May 2, 2019, 6:11 a.m.