کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
566554 | 875994 | 2013 | 9 صفحه PDF | دانلود رایگان |

Ranking relevance degree information is widely utilized in the ranking models of information retrieval applications, such as text and multimedia retrieval, question answering, and visual search reranking. However, existing feature dimensionality reduction methods neglect this kind of valuable potential supervised information. In this paper, we extend the pairwise constraints from the traditional class labels to ranking relevance degrees, and propose a novel dimensionality reduction method called Rank-CCA. Rank-CCA effectively incorporates ranking relevance constraints into standard canonical correlation analysis (CCA) algorithm, and is able to employ the knowledge of both unlabeled and labeled data. In the application of visual search reranking, our proposed method is verified through extensive experimental studies. Experimental results show that Rank-CCA is superior to standard CCA and semi-supervised CCA (Semi-CCA) algorithm, and achieves comparable performance with several state-of-the-art reranking methods while preserving the superiority of low dimensional features.
► We incorporate ranking relevance constraints into canonical correlation analysis.
► Both labeled and unlabeled data are employed.
► The proposed Rank-CCA method is used for image search reranking.
Journal: Signal Processing - Volume 93, Issue 8, August 2013, Pages 2352–2360