Ranking Fisher discriminant analysis

Existing feature dimensionality reduction algorithms are inherently designed for the case of classification, clustering and retrieval, but not for ranking applications such as visual search reranking. This is a serious limitation which restricts the applicability of existing dimensionality reduction methods as well as the generalization ability of ranking applications. Therefore, it is important to design a kind of special methods to be effectively employed for ranking applications. Fisher discriminant analysis (FDA) is one of the most popular dimensionality reduction methods. Thus, we propose a novel dimensionality reduction algorithm based on FDA to solve this kind of problem in this paper. Specifically, relevance degree information—the data label in ranking applications, is introduced to a semi-supervised form of FDA, in which both local information and unlabeled data are employed. We name the proposed method as ranking Fisher discriminant analysis (RFDA). To verify the effectiveness of RFDA, extensive experiments are carried out on image search reranking applications, which show significant performance based on the popular MSRA-MM dataset.