Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
Image retagging aims to improve tag quality of social images by refining their original tags or assigning new high-quality tags. Recent approaches simultaneously explore visual, user and tag information to improve the performance of image retagging by constructing and exploring an image-tag-user graph. However, such methods will become computationally infeasible with the rapidly increasing number of images, tags and users. It has been proven that Anchor Graph Regularization (AGR) can significantly accelerate large-scale graph learning model by exploring only a small number of anchor points. Inspired by this, we propose a novel Social anchor-Unit GrAph Regularized Tensor Completion (SUGAR-TC) method to effectively refine the tags of social images, which is insensitive to the scale of the applied data. First, we construct an anchor-unit graph across multiple domains (e.g., image and user domains) rather than traditional anchor graph in a single domain. Second, a tensor completion based on SUGAR is implemented on the original image-tag-user tensor to refine the tags of the anchor images. Third, we efficiently assign tags to non-anchor images by leveraging the relationship between the non-anchor images and the anchor units. Experimental results on a real-world social image database well demonstrate the effectiveness of SUGAR-TC, outperforming several related methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph LearningTAGSimilar Papers 제목 키워드 기반
Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods
Community detection in graphs has been extensively studied both in theory and in applications. However, detecting communities in hypergraphs is more challenging. In this paper, we propose a tensor decomposition approach …
Community DetectionTAGTensor DecompositionInterpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization
The clustering method based on the anchor graph has gained significant attention due to its exceptional clustering performance and ability to process large-scale data. One common approach is to learn bipartite graphs wit…
ClusteringRegularized Orthogonal Tensor Decompositions for Multi-Relational Learning
Multi-relational learning has received lots of attention from researchers in various research communities. Most existing methods either suffer from superlinear per-iteration cost, or are sensitive to the given ranks. To …
Relational ReasoningCommunity Detection in Partially Observable Social Networks
The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks' topology and functions. However, most social network data are col…
Community DetectionFast Hypergraph Regularized Nonnegative Tensor Ring Factorization Based on Low-Rank Approximation
For the high dimensional data representation, nonnegative tensor ring (NTR) decomposition equipped with manifold learning has become a promising model to exploit the multi-dimensional structure and extract the feature fr…