Abstract: In multi-view learning, optimized prediction matrices are determined for V?2 views of n objects, and a prediction of a view of an object is generated based on the optimized prediction matrix for that view. An objective is optimized, wherein is a set of parameters including at least the V prediction matrices and a concatenated matrix comprising a concatenation of the prediction matrices, and comprises a sum including at least a loss function for each view, a trace norm of the prediction matrix for each view, and a trace norm of the concatenated matrix. may further include a sparse matrix for each view, with further including an element-wise norm of the sparse matrix for each view. may further include regularization parameters scaling the trace norms of the prediction matrices and the trace norm of the concatenated matrix.

Abstract: In multi-view learning, optimized prediction matrices are determined for V?2 views of n objects, and a prediction of a view of an object is generated based on the optimized prediction matrix for that view. An objective is optimized, wherein is a set of parameters including at least the V prediction matrices and a concatenated matrix comprising a concatenation of the prediction matrices, and comprises a sum including at least a loss function for each view, a trace norm of the prediction matrix for each view, and a trace norm of the concatenated matrix. may further include a sparse matrix for each view, with further including an element-wise norm of the sparse matrix for each view. may further include regularization parameters scaling the trace norms of the prediction matrices and the trace norm of the concatenated matrix.