Description Usage Arguments Details Value Author(s) References See Also Examples
The qkernel Sammon Mapping is an implementation for Sammon mapping, one of the earliest dimension reduction techniques that aims to find low-dimensional embedding that preserves pairwise distance structure in high-dimensional data space. qsammon is a nonlinear form of Sammon Mapping.
1 2 3 4 5 6 7 8 9 10 11 12 | ## S4 method for signature 'matrix'
qsammon(x, kernel = "rbfbase", qpar = list(sigma = 0.5, q = 0.9),
dims = 2, Initialisation = 'random', MaxHalves = 20,
MaxIter = 500, TolFun = 1e-7, na.action = na.omit, ...)
## S4 method for signature 'cndkernmatrix'
qsammon(cndkernel, x, k, dims = 2, Initialisation = 'random',
MaxHalves = 20,MaxIter = 500, TolFun = 1e-7, ...)
## S4 method for signature 'qkernmatrix'
qsammon(qkernel, x, k, dims = 2, Initialisation = 'random',
MaxHalves = 20, MaxIter = 500, TolFun = 1e-7, ...)
|
x |
the data matrix indexed by row or a kernel matrix of |
kernel |
the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:
The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. |
qpar |
the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :
Hyper-parameters for user defined kernels can be passed through the qpar parameter as well. |
qkernel |
the kernel function to be used to calculate the qkernel matrix. |
cndkernel |
the cndkernel function to be used to calculate the CND kernel matrix. |
k |
the dimension of the original data. |
dims |
Number of features to return. (default: 2) |
Initialisation |
|
MaxHalves |
maximum number of step halvings. (default : 20) |
MaxIter |
the maximum number of iterations allowed. (default : 500) |
TolFun |
relative tolerance on objective function. (default : 1e-7) |
na.action |
A function to specify the action to be taken if |
... |
additional parameters |
Using kernel functions one can efficiently compute
principal components in high-dimensional
feature spaces, related to input space by some non-linear map.
The data can be passed to the qsammon
function in a matrix
, in addition qsammon
also supports input in the form of a
kernel matrix of class qkernmatrix
or class cndkernmatrix
.
dimRed |
The matrix whose rows are embedded observations. |
kcall |
The function call contained |
cndkernf |
The kernel function used |
all the slots of the object can be accessed by accessor functions.
Yusen Zhang
yusenzhang@126.com
Sammon, J.W. (1969) A Nonlinear Mapping for Data Structure Analysis. IEEE Transactions on Computers, C-18 5:401-409.
1 2 3 4 5 6 7 8 | data(iris)
train <- as.matrix(iris[,1:4])
labeltrain<- as.integer(iris[,5])
## S4 method for signature 'matrix'
kpc2 <- qsammon(train, kernel = "rbfbase", qpar = list(sigma = 2, q = 0.9), dims = 2,
Initialisation = 'pca', TolFun = 1e-5)
plot(dimRed(kpc2), col = as.integer(labeltrain))
cndkernf(kpc2)
|
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