When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores
Name:
SparCod-Rank NCAA R1 170427.pdf
Size:
218.9Kb
Format:
PDF
Description:
Accepted Manuscript
Type
ArticleKAUST Department
Computational Bioscience Research Center (CBRC)Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Date
2017-06-28Online Publication Date
2017-06-28Print Publication Date
2019-03Permanent link to this record
http://hdl.handle.net/10754/625649
Metadata
Show full item recordAbstract
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.Citation
Wang JJ-Y, Cui X, Yu G, Guo L, Gao X (2017) When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores. Neural Computing and Applications. Available: http://dx.doi.org/10.1007/s00521-017-3102-9.Sponsors
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) and the National Natural Science Foundation of China under the Grant No. 61502463.Publisher
Springer NatureAdditional Links
http://link.springer.com/article/10.1007/s00521-017-3102-9ae974a485f413a2113503eed53cd6c53
10.1007/s00521-017-3102-9