select_matrix_list_columns

Select x_list columns specified by (conformable) list of indices

Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).

Balasubramanian Narasimhan

multiview

Cooperative Learning for Multi-View Analysis

Daisy Yi Ding 

Robert J. Tibshirani

Trevor Hastie

Kenneth Tay

James Yang

select_matrix_list_columns function

<dl><dt>x_list</dt>
<dd>a list of <code>x</code> matrices with same number of rows
<code>nobs</code></dd>
<dt>indices</dt>
<dd>a vector of indices in <code>1:nvars</code></dd></dl>

Arguments

Select x_list columns specified by (conformable) list of indices — select_matrix_list_columns

<dl>

<dt>x_list</dt>
<dd>a list of <code>x</code> matrices with same number of rows
<code>nobs</code></dd>


<dt>indices</dt>
<dd>a vector of indices in <code>1:nvars</code></dd>

</dl>

select_matrix_list_columns: Select x_list columns specified by (conformable) list of indices

Description

Usage

Value

Arguments