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

TL;DR

This paper introduces a novel joint framework that integrates sparse coding and ranking score learning, leveraging local linear approximations in the sparse code space to improve data representation and retrieval accuracy.

Contribution

It is the first to propose a unified algorithm that simultaneously learns sparse codes, a dictionary, and ranking scores by exploiting local relationships.

Findings

Joint sparse coding and ranking score learning improves retrieval performance.

The proposed iterative algorithm effectively optimizes the unified objective.

Local linear approximation in sparse code space bridges coding and ranking problems.

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.