document: A new multi-view regression approach with an application to customer wallet estimation Motivated by the problem of customer wallet estimation , we propose a new setting for multi-view regression , where we learn a completely unobserved target ( in our case , customer wallet ) by modeling it as a `` central link '' in a directed graphical model , connecting multiple sets of observed variables . The resulting conditional independence allows us to reduce the maximum discriminative likelihood estimation problem to a convex optimization problem for exponential linear models . We show that under certain modeling assumptions , in particular , when there exist two conditionally independent views and the noise is Gaussian , this problem can be reduced to a single least squares regression . Thus , for this specific , but widely applicable setting , the `` unsupervised '' multi-view problem can be solved via a simple supervised learning approach . This reduction also allows us to test the statistical independence assumptions underlying the graphical model and perform variable selection . We demonstrate the effectiveness of our approach on our motivating problem of customer wallet estimation and on simulation data .
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