make_row

Build a block row matrix for multiview

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

make_row function

<dl><dt>x_list</dt>
<dd>list of x matrices</dd>
<dt>p_x</dt>
<dd>a list of ncol of elements in x_list</dd>
<dt>pair</dt>
<dd>an integer vector of two indices</dd>
<dt>rho</dt>
<dd>the rho value</dd></dl>

Arguments

Build a block row matrix for multiview — make_row

<dl>

<dt>x_list</dt>
<dd>list of x matrices</dd>


<dt>p_x</dt>
<dd>a list of ncol of elements in x_list</dd>


<dt>pair</dt>
<dd>an integer vector of two indices</dd>


<dt>rho</dt>
<dd>the rho value</dd>

</dl>

make_row: Build a block row matrix for multiview

Description

Usage

Value

Arguments