When coding meets ranking: A joint framework based on local learning

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, learn the ranking scores from data points plays an important role. Up to new, these two methods have always been used individually, 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 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 points, 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, reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct an unified objective function for learning of sparse codes, dictionary and rankings scores. An iterative algorithm is developed to optimize the objective function to jointly learn the sparse codes, dictionary and rankings scores.