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A Novel Multiple Kernel Learning Framework for Heterogeneous Feature Fusion and Variable Selection
IEEE transactions on multimedia
We propose a novel multiple kernel learning (MKL) algorithm with a group lasso regularizer, called group lasso regularized MKL (GL-MKL), for heterogeneous feature fusion and variable selection. For problems of feature fusion, assigning a group of base kernels for each feature type in an MKL framework provides a robust way in fitting data extracted from different feature domains. Adding a mixed 1,2 norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at thedoi:10.1109/tmm.2012.2188783 fatcat:l2iqmss3z5ex3dpo7ua27vofqy