Discovering the Latent Similarities of the KNN Graph by Metric Transformation

The manifold of the dataset turns out to be quite useful in refining the retrieval results, and the diffusion process provides an efficient solution by careful selection of the similarity neighborhood which is usually modeled as the K-nearest neighborhood (KNN) graph. However, existing works are sensitive to the topology noises induced by the first K neighbors. In this paper, we tackle the problem by studying metric transformation which aims at finding new functional relationship to dig the latent similarity. The advantage of the approach lies in its robustness towards the varying K values; that is to say, it could preserve high similarity performances even if K is very large. Except for discussing only the global KNN (i.e. the same K for all neighborhoods) graph, we also investigate to specify a different K for each neighborhood by incorporating the new penalized consensus information (PCI). We show that PCI works superior compared with the original consensus information for denoising. Experiments on multiple affinity matrices have corroborated the superiority of our method with surprising good results.