Ranking Fisher discriminant analysis

作者：Ji Zhong^*; Jing Peiguang; Yu Tianshi; Su Yuting; Liu Changshu

来源：Neurocomputing, 2013, 120: 54-60.

DOI：10.1016/j.neucom.2012.07.052

• Feature dimensionality reduction
• Fisher discriminant analysis
• Relevance degree information
• Image search reranking

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

• 出版日期2013-11-23
• 单位南京大学; 天津大学