Smooth Neighborhood Structure Mining on Multiple Affinity Graphs with Applications to Context-Sensitive Similarity

Due to the ability of capturing geometry structures of the data manifold, diffusion process has demonstrated impressive performances in retrieval task by spreading the similarities on the affinity graph. In view of robustness to noise edges, diffusion process is usually localized, i.e., only propagating similarities via neighbors. However, selecting neighbors smoothly on graph-based manifolds is more or less ignored by previous works. In this paper, we propose a new algorithm called Smooth Neighborhood (SN) that mines the neighborhood structure to satisfy the manifold assumption. By doing so, nearby points on the underlying manifold are guaranteed to yield similar neighbors as much as possible. Moreover, SN is adjusted to tackle multiple affinity graphs by imposing a weight learning paradigm, and this is the primary difference compared with related works which are only applicable with one affinity graph. Exhausted experimental results and comparisons against other algorithms manifest the effectiveness of the proposed algorithm.