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Classification methods that leverage the strengths of data from multiplesources (multi-view data) simultaneously have enormous potential to yield morepowerful findings than two step methods: association followed byclassification. We propose two methods, sparse integrative discriminantanalysis (SIDA) and SIDA with incorporation of network information (SIDANet),for joint association and classification studies. The methods consider theoverall association between multi-veiw data, and the separation within eachview in choosing discriminant vectors that are associated and optimallyseparate subjects into different classes. SIDANet is among the first methods toincorporate prior structural information in joint association andclassification studies. It uses the normalized Laplacian of a graph to smoothcoefficients of predictor variables, thus encouraging selection of predictorsthat are connected and behave similarly. We demonstrate the effectiveness ofour methods on a set of synthetic and real datasets. Our findings underscorethe benefit of joint association and classification methods if the goal is tocorrelate multi-view data and to perform classification.

Sparse Linear Discriminant Analysis for Multi-view Structured Data