A simple feature combination method based on dominant sets

• We present a simple weighting scheme for feature combination based dominant sets.
• We use dominant sets to evaluate how accurate a kernel matrix is expected to be.
• This accuracy is found to be related to the classification performance.
• We validate the effectiveness of our method with extensive experiments.

Feature combination is a popular method for improving object classification performances. In this paper we present a simple and effective weighting scheme for feature combination based on the dominant-set notion of a cluster. Specifically, we use dominant sets clustering to evaluate how accurate a kernel matrix is expected to be for a SVM classifier. This expected kernel accuracy reflects the discriminative power of the kernel matrix and thus used in weighting the kernel matrix in feature combination. Our method is simple, intuitive, memory and computation efficient, and performs comparably to the popular and sophisticated optimization based methods. We conduct experiments with several datasets of diverse object types and validate the effectiveness of the proposed method. In fact, in five out of the six datasets used in our experiments, we obtained the best results until now in our knowledge.