

   IInnddeeppeennddeenntt CCoommppoonneenntt AAnnaallyyssiiss

        ica(X, lrate, epochs=100, ncomp=dim(X)[2], fun="negative")

   AArrgguummeennttss::

          X: The matrix for which the ICA is to be computed

      lrate: learning rate

     epochs: number of iterations

      ncomp: number of independent components

        fun: function used for the nonlinear computation part

   DDeessccrriippttiioonn::

        This is an R-implementation of the Matlab-Function of
        Petteri.Pajunen@hut.fi.

        For a data matrix X independent components are
        extracted by applying a nonlinear PCA algorithm. The
        parameter `fun' determines which nonlinearity is used.
        `fun' can either be a function or one of the following
        strings "negative kurtosis", "positive kurtosis", "4th
        moment" which can be abbreviated to uniqueness. If
        `fun' equals "negative (positive) kurtosis" the func-
        tion tanh (x-tanh(x)) is used which provides ICA for
        sources with negative (positive) kurtosis. For `fun ==
        "4th moments"' the signed square function is used.

   VVaalluuee::

        An object of class "ica" containing

    weights: ICA weight matrix

   projection: Projected data

     epochs: Number of iterations

        fun: Name of the used function

      lrate: Learning rate used

   initweights: Initial weight matrix

   NNoottee::

        Currently, there is no reconstruction from the ICA sub-
        space to the original input space.

   AAuutthhoorr((ss))::

        Andreas Weingessel

   RReeffeerreenncceess::

        Oja et al., "Learning in Nonlinear Constrained Hebbian
        Networks", in Proc. ICANN-91, pp. 385-390.

        Karhunen and Joutsensalo, "Generalizations of Principal
        Component Analysis, Optimization Problems, and Neural
        Networks", Neural Networks, v. 8, no. 4, pp. 549-562,
        1995.

   SSeeee AAllssoo::

   EExxaammpplleess::

