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Augmented Kernel Matrix (AKM) has recently been proposed to accommodate for the fact that a single training example may have different importance in different feature spaces, in contrast to Multiple Kernel Learning (MKL) that assigns the same weight to all examples in one feature space. However, the AKM approach is limited to small datasets due to its memory requirements. An alternative way to fuse information from different feature channels is classifier fusion (ensemble methods). There is a significant amount of work on linear programming formulations of classifier fusion (CF) in the case of binary classification. In this paper we derive primal and dual of AKM to draw its correspondence with CF. We propose a multiclass extension of binary n-LPBoost, which learns the contribution of each class in each feature channel. Existing approaches of CF promote sparse features combinations, due to regularization based on ‘1-norm, and lead to a selection of a subset of feature channels, which is not good in case of informative channels. We also generalize existing CF formulations to arbitrary ‘p-norm for binary and multiclass problems which results in more effective use of complementary information. We carry out an extensive comparison and show that the proposed nonlinear CF schemes outperform its sparse counterpart as well as state-of-the-art MKL approaches.

Augmented Kernel Matrix vs Classifier Fusion for Object Recognition