Robust Graph Structure Learning for Multimedia Data Analysis

Wei Zhou,Shaojie Qiao,Nan Han,Wei Guo,Zhaoxuan Gong,Yuanlong Cao

doi:10.1155/2021/9458188

Abstract

With the rapid development of computer network technology, we can acquire a large amount of multimedia data, and it becomes a very important task to analyze these data. Since graph construction or graph learning is a powerful tool for multimedia data analysis, many graph-based subspace learning and clustering approaches have been proposed. Among the existing graph learning algorithms, the sample reconstruction-based approaches have gone the mainstream. Nevertheless, these approaches not only ignore the local and global structure information but also are sensitive to noise. To address these limitations, this paper proposes a graph learning framework, termed Robust Graph Structure Learning (RGSL). Different from the existing graph learning approaches, our approach adopts the self-expressiveness of samples to capture the global structure, meanwhile utilizing data locality to depict the local structure. Specially, in order to improve the robustness of our approach against noise, we introduce l 2 , 1 -norm regularization criterion and nonnegative constraint into the graph construction process. Furthermore, an iterative updating optimization algorithm is designed to solve the objective function. A large number of subspace learning and clustering experiments are carried out to verify the effectiveness of the proposed approach.

Highlights

With the rapid growth of information technology and computer network technology, a large number of multimedia data can be collected from many research fields such as computer vision, image processing, and natural language processing
Subspace learning and clustering tasks are employed for verifying the effectiveness of the proposed approach
This paper proposes a novel graph learning framework, named Robust Graph Structure Learning (RGSL) for effective multimedia data analysis

Summary

Introduction

With the rapid growth of information technology and computer network technology, a large number of multimedia data can be collected from many research fields such as computer vision, image processing, and natural language processing. Learning or constructing a valuable graph to describe the pairwise similarity or relationship among the samples is a key issue to multimedia data analysis [7]. A series of graph learning approaches have been proposed in which the heat-kernel function is the most widely used graph construction manner, such as k-nearestneighborhood graph (k-NN graph) or ε-nearest-neighborhood graph (ε graph). The edges of vertexes are computed based on the Euclidean distance among samples and the weights of the edge between two vertexes are estimated by the heat kernel [8]. The processes of neighbor selection and weight calculation are independent, which are sensitive to noise and often cannot well reveal the real similarities of samples [10]

Methods

Results

Conclusion