reshape_x_to_xlist

Return a new list of x matrices of same shapes as those in x_list

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

reshape_x_to_xlist function

<dl><dt>x</dt>
<dd>the column-binded entries of <code>x_list</code></dd>
<dt>x_list</dt>
<dd>a list of <code>x</code> matrices with same number of rows
<code>nobs</code></dd></dl>

Arguments

Return a new list of x matrices of same shapes as those in x_list — reshape_x_to_xlist

<dl>

<dt>x</dt>
<dd>the column-binded entries of <code>x_list</code></dd>


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

</dl>

reshape_x_to_xlist: Return a new list of x matrices of same shapes as those in x_list

Description

Usage

Arguments