Solved: Suggestions To Fix R-Kernel PCA Example

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    Description

    Principal component analysis of a kernel is a non-linear form related to the fundamentalComponent analysis.

    Use

     Method # S4 for formulaskpca (x, = data NULL, na.action, ...) 

    # S4 method for matrixkpca (x, kernel = Kpar "rbfdot", = list (sigma = 0,1), = features 0, th means 1e-4, na.action = na.omit, ...)

    # s4 method for kernelMatrixkpca (x, features is 0, th = 1e-4, ...)

    # S4 method for listkpca (x, kernel is "stringdot", = kpar list (length = 4, lambda is 0.5), equals 0, th = 1e-4, na.action means na.omit, ...)

    Arguments

    x

    the data matrix found for each row, or a formula containing Model or just a kernel matrix with the speed kernelMatrix or a list of charm vectors

    data

    optional frame data with reasons inSimulate this (if formula).

    Kernel

    kernel function used in learning predictions and. This parameter can be set with any kernel class function that computes the dot product with respect to two Vector arguments. kernlab offers the most common kernel featureswhich can be used very well by setting a kernel parameter to be as follows Channels:

    • rbfdot The core of the radial basis follows the “Gaussian”

    • polydot Kernel polynomial function

    • vanilladot Linear Kernel Function

    • tanhdot Tangent hyperbolic kernel function

    • Laplacedot Laplace kernel function

    • besseldot Bessel kernel function

    • anovadot RBF Anova core function

    • splinedot Spline kernel

    A kernel parameter can sometimes refer to a single UDF. can be customized Kernel class by passing part of the function name as an argument.

    kpar

    hyperparameters report (kernel parameters). Here is a list of the types of parameters used The main function. Valid parameters for existing corn kernels:

    • sigma reverse kernel for larger radial base Kernel function “rbfdot” as well as Laplace kernel “laplacedot”.

    • Degree, scale, offset relative to the kernel of the “polydot” polynomial

    • Scale, offset relative to the core of the hyperbolic tangent Successes with “tanhdot”

    • Sigma, order, degree for the Bessel kernel “besseldot”.

    • Sigma, degree , which is for ANOVA core “anovadot”.

    Hyperparameter users for specific kernels are easily accessible via kpar parameters also.

    Characteristics

    Number with feature components) (main for To revert to. (Default: 0 – all)

    th

    eigenvalue is below and this is also basic Things are ignored as valid (only when features = 0). (Default: 0.0001)

    na.action

    Function for specifying procedures to follow when < code> NA s is find. The default action is na.omit , which will reject hits. with missing values ​​for each important variable. Alternative na.fail leading to unintended NA casesto be found. If (Note: specified, this argument must have a name.)

    Value

    the s4 object that contains the baseSome of the vectors, as well as the same eigenvalues.

    pcv

    matrix containing the main element of vectors (column sage)

    eig

    Associated eigenvalues ​​

    rotates

    Actual data is projected (rotated) onto required components

    xmatrix

    Source Data Matrix

    all slots of objects can be called through accessor functions.

    Details

    With kernel 1, functions can be efficiently computed Key Features in High Dimension Entity plots are linked to the input space through a series of nonlinear maps. Data can of course be sent to celebrate kpca in a matrix a or a. to be delivered data.frame , the kpca addon also provides input help asKernel matrix in class kernelMatrix or as a list of subscriber symbolsVectors requiring the use of a chained kernel.

    Sources

    Schoelkopf B., A. Smola, K.-R. Müller: Nonlinear component as analysis of a problem with basic eigenvalues Nerve camJune 10, 1299-1319 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.1366

    See Also

    kcca , PC

    Examples

     # DO NOT EXECUTE# another example of a new irisData (iris)Test <- sample (1: 150.20)kpc <- kpca (~., data = iris [-test, -5], kernel = "rbfdot",            kpar = list (sigma = 0.2), functions = 2)# print head vectorpcv (pda)# Track information projector component by componentgraph (rotation (kpc), col = as.integer (iris [-test, 5]),     xlab = "1st main component", ylab = "2nd main component")# Remaining scores are integratedemb <- forecasts (kpc, iris [test, -5])Points (emb, col = as.integer (iris [test, 5]))#

    is a matrix file that is indexed according to a formula or briefly describes a formula that Kernel model or class matrix kernelMatrix or case of character vectors

    additional bike frame for transferring data with variables inoften a model (when using a formula).

    A working kernel used in training and therefore in forecasting. This parameter can also be set for any function linkednoah with a kernel class that computes dept. product moved between two Vector arguments. kernlab offers many common kernel features sometimes it can be used with a kernel parameter, usually like this Channels:

    • rbfdot Gaussian radial core kernel function

    • polydot Kernel polynomial function

    • vanilladot Linear Kernel Function

    • tanhdot Tangent hyperbolic kernel function

    • Laplacedot Laplace kernel function

    • besseldot Bessel kernel function

    • anovadot RBF Anova core function

    • splinedot Spline kernel

    r kernel pca example

    A kernel parameter can also be a user-defined parameter Program the kernel by passing the name of the function with one argument.

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    list of hyperparameters associated with (kernel parameters). This is usually a list of features to be used with The main function. Valid ranges for existing kernels:

    • sigma inverse kernel for radial base width The position of the "rbfdot" kernel and the La kernelplaza "laplacedot".

    • Degree, scale, offset for the "polydot" polynomial

    • Scale, offset for the kernel of the hyperbolic tangent Tankdot function

    • Sigma, order, degree for the Bessel kernel "besseldot".

    • Sigma, degree of a person for ANOVA core "Anovadot".

    Hyper settings users only for certain kernels can be used in. to be delivered The kpar parameter is just that good.

    Number of functions (main components) up to To return to. (Default: nothing, everything)

    the value of the part of the eigenvalue under the main Components are not taken into account (valid only if characteristics are equal to 0). (Default: 0.0001)

    r kernel pca example

    Function if you want to specify the action to be carried over if NA is s find. The default action could be na.omit , resulting in rejection due to cases with missing values ​​for each required variable. Alternativeequals na.fail , which raises 1 on error NA It remains to find. If (Note: given , this statement must be specified.)

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