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>).

Hypothesis Test for Dependent Clusterings of Two Data Views
person("Lucy L.", "Gao", email = "lucygao@uw.edu", role = c("aut", "cre"))
Implements a hypothesis test of whether clusterings of two data views are independent from Gao, L.L., Bien, J., and Witten, D. (2019) Are Clusterings of Multiple Data Views Independent? Biostatistics <DOI:10.1093/biostatistics/kxz001>.

Hypothesis Tests for Association Between Subgroups in Two Data
Views
Tests for association between subgroups in two multivariate data views, two network data views, or a multivariate data view and a network data view. (Reference 1 is Gao, L.L., Bien, J., and Witten, D. (2019) Are Clusterings of Multiple Data Views Independent? to appear in Biostatistics, Reference 2 is Gao, L.L., Witten, D., Bien, J. Testing for Association in Multi-View Network Data, preprint.)
Matrix, stats, mclust, matrixStats, randnet, irlba,
doParallel, foreach

person("Lucy L.", "Gao", email = "lucy.gao@uwaterloo.ca", role = c("aut", "cre"))
Tests for association between subgroups in two multivariate data views, two network data views, or a multivariate data view and a network data view. (Reference 1: Gao, L.L., Bien, J., and Witten, D. (2020) "Are Clusterings of Multiple Data Views Independent?", <doi:10.1093/biostatistics/kxz001> and Reference 2:  Gao, L.L., Witten, D., Bien, J. (2021) Testing for Association in Multi-View Network Data, <doi:10.1111/biom.13464>.)