Description Usage Arguments Details Value Note Author(s) References See Also Examples
The qkernel Generalized Discriminant Analysis is a method that deals with nonlinear discriminant analysis using kernel function operator.
1 2 3 4 5 6 7 8 | ## S4 method for signature 'matrix'
qkgda(x, label, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
features = 0, th = 1e-4, na.action = na.omit, ...)
## S4 method for signature 'cndkernmatrix'
qkgda(x, label, features = 0, th = 1e-4, na.action = na.omit, ...)
## S4 method for signature 'qkernmatrix'
qkgda(x, label, features = 0, th = 1e-4, ...)
|
x |
the data matrix indexed by row, or a kernel matrix of |
label |
The original labels of the samples. |
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. |
features |
Number of features (principal components) to return. (default: 0 , all) |
th |
the value of the eigenvalue under which principal components are ignored (only valid when features = 0). (default : 0.0001) |
na.action |
A function to specify the action to be taken if |
... |
additional parameters |
The qkernel Generalized Discriminant Analysis method provides a mapping of the input vectors into high dimensional feature space, generalizing the classical Linear Discriminant Analysis to non-linear discriminant analysis.
The data can be passed to the qkgda
function in a matrix
, in addition qkgda
also supports input in the form of a
kernel matrix of class qkernmatrix
or class cndkernmatrix
.
An S4 object containing the eigenvectors and their normalized projections, along with the corresponding eigenvalues and the original function.
prj |
The normalized projections on eigenvectors) |
eVal |
The corresponding eigenvalues |
eVec |
The corresponding eigenvectors |
kcall |
The formula of the function called |
cndkernf |
The kernel function used |
xmatrix |
The original data matrix |
all the slots of the object can be accessed by accessor functions.
The predict function can be used to embed new data on the new space
Yusen Zhang
yusenzhang@126.com
1.Baudat, G, and F. Anouar:
Generalized discriminant analysis using a kernel approach
Neural Computation 12.10(2000),2385
2.Deng Cai, Xiaofei He, and Jiawei Han:
Speed Up Kernel Discriminant Analysis
The VLDB Journal,January,2011,vol.20, no.1,21-33.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c("1","2","3"), rep(50,3)))
testset <- sample(1:150,20)
train <- as.matrix(iris[-testset,-5])
test <- as.matrix(iris[testset,-5])
Sp = rep(c("1","2","3"), rep(50,3))
labels <-as.numeric(Sp)
trainlabel <- labels[-testset]
testlabel <- labels[testset]
kgda1 <- qkgda(train, label=trainlabel, kernel = "ratibase", qpar = list(c=1,q=0.9),features = 2)
prj(kgda1)
eVal(kgda1)
eVec(kgda1)
kcall(kgda1)
# xmatrix(kgda1)
#print the principal component vectors
prj(kgda1)
#plot the data projection on the components
plot(kgda1@prj,col=as.integer(train), xlab="1st Principal Component",ylab="2nd Principal Component")
|
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