When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

Jim Jing-Yan Wang, Xuefeng Cui, Ge Yu, Lili Guo, Xin Gao

Research output: Contribution to journalArticlepeer-review

Abstract

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.
Original languageEnglish (US)
Pages (from-to)701-710
Number of pages10
JournalNeural Computing and Applications
Volume31
Issue number3
DOIs
StatePublished - Jun 28 2017

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