Feature extraction by using reconstruction ICA

returns
a reconstruction independent component analysis (RICA) model object
that contains the results from applying RICA to the table or matrix
of predictor data `Mdl`

= rica(`X`

,`q`

)`X`

containing *p* variables. `q`

is
the number of features to extract from `X`

, therefore `rica`

learns
a *p*-by-`q`

matrix of transformation
weights. For undercomplete or overcomplete feature representations, `q`

can
be less than or greater than the number of predictor variables, respectively.

To access the learned transformation weights, use

`Mdl.TransformWeights`

.To transform

`X`

to the new set of features by using the learned transformation, pass`Mdl`

and`X`

to`transform`

.

uses
additional options specified by one or more `Mdl`

= rica(`X`

,`q`

,`Name,Value`

)`Name,Value`

pair
arguments. For example, you can standardize the predictor data or
specify the value of the penalty coefficient in the reconstruction
term of the objective function.

The `rica`

function creates a linear transformation
of input features to output features. The transformation is based
on optimizing a nonlinear objective function that roughly balances
statistical independence of the output features versus the ability
to reconstruct the input data using the output features.

For details, see Reconstruction ICA Algorithm.