kpca-class              package:kernlab              R Documentation

_C_l_a_s_s "_k_p_c_a"

_D_e_s_c_r_i_p_t_i_o_n:

     The Kernel Principal Components Analysis class

_O_b_j_e_c_t_s _o_f _c_l_a_s_s "_k_p_c_a":

     Objects can be created by calls of the form 'new("kpca", ...)'. or
     by calling the 'kpca' function.

_S_l_o_t_s:

     '_p_c_v': Object of class '"matrix"' containing the principal
          component vectors 

     '_e_i_g': Object of class '"vector"' containing the coresponding
          eigenvalues

     '_r_o_t_a_t_e_d': Object of class '"matrix"' containing the projection of
          the data on the principal components

     '_k_e_r_n_e_l_f': Object of class '"function"' containing the kernel
          function used

     '_k_p_a_r': Object of class '"list"' containing the kernel parameters
          used 

     '_x_m_a_t_r_i_x': Object of class '"matrix"' conatining the data matrix
          used 

     '_k_c_a_l_l': Object of class '"ANY"' containing the function call 

     '_n._a_c_t_i_o_n': Object of class '"ANY"' containg the action performed
          on NA 

_M_e_t_h_o_d_s:


     _e_i_g 'signature(object = "kpca")': returns the eigenvalues 

     _k_c_a_l_l 'signature(object = "kpca")': returns the performed call

     _k_e_r_n_e_l_f 'signature(object = "kpca")': returns the used kernel
          function

     _p_c_v 'signature(object = "kpca")': returns the principal component
          vectors 

     _p_r_e_d_i_c_t 'signature(object = "kpca")': embeeds new data 

     _r_o_t_a_t_e_d 'signature(object = "kpca")': returns the projected data

     _x_m_a_t_r_i_x 'signature(object = "kpca")': returns the used data matrix 

_A_u_t_h_o_r(_s):

     Alexandros Karatzoglou
      alexandros.karatzoglou@ci.tuwien.ac.at

_S_e_e _A_l_s_o:

     'ksvm-class',  'kcca-class'

_E_x_a_m_p_l_e_s:

     # another example using the iris
     data(iris)
     test <- sample(1:50,20)

     kpc <- kpca(~.,data=iris[-test,-5],kernel="rbfdot",kpar=list(sigma=0.2),features=2)

     #print the principal component vectors
     pcv(kpc)
     rotated(kpc)
     kernelf(kpc)
     eig(kpc)

