Jing Wang

Jing Wang
University of Greenwich · School of Computing & Mathematical Sciences

Doctor of Philosophy

About

29
Publications
7,737
Reads
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1,412
Citations
Citations since 2016
28 Research Items
1411 Citations
2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
Additional affiliations
September 2018 - present
The University of Tokyo
Position
  • PostDoc Position
Description
  • Homepage: http://www.escience.cn/people/JingWangRegina/index.html
September 2014 - February 2017
Bournemouth University
Position
  • PhD Student
Education
September 2010 - February 2012
City University of Hong Kong
Field of study
  • Multimedia information technology

Publications

Publications (29)
Conference Paper
Graph embedding, which represents real-world entities in a mathematical space, has enabled numerous applications such as analyzing natural languages, social networks, biochemical networks, and knowledge bases. It has been experimentally shown that graph embedding in hyperbolic space can represent hierarchical tree-like data more effectively than em...
Preprint
Full-text available
Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the form of entity i is more similar to entity j than to entity k. It has been experimentally shown that HOE can obtain representations of hierarchical data such as a knowledge base and a citation net...
Article
Full-text available
Multi-label learning deals with data examples which are associated with multiple class labels simultaneously. Despite the success of existing approaches to multi-label learning, there is still a problem neglected by researchers, i.e., not only are some of the values of observed labels missing, but also some of the labels are completely unobserved f...
Article
Full-text available
Self-representation based subspace learning has shown its effectiveness in many applications. In this paper, we promote the traditional subspace representation learning by simultaneously taking advantages of multiple views and prior constraint. Accordingly, we establish a novel algorithm termed as Tensorized Multi-view Subspace Representation Learn...
Conference Paper
Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hide in the data, especially for large-scale data sets. Discovering and exploring the...
Conference Paper
Existing methods on representation-based subspace clustering mainly treat all features of data as a whole to learn a single self-representation and get one clustering solution. Real data however are often complex and consist of multiple attributes or sub-features, such as a face image has expressions or genders. Each attribute is distinct and compl...
Conference Paper
Prediction of glaucomatous visual field loss has significant clinical benefits because it can help with early detection of glaucoma as well as decision-making for treatments. Glaucomatous visual loss is conventionally captured through visual field sensitivity (VF ) measurement, which is costly and time-consuming. Thus, existing approaches mainly pr...
Article
A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph. We learn a consensus graph with minimizing disagreement between differen...
Conference Paper
Full-text available
Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially , an effective and accurate representation should be able to uncover and preserve the true data structure. Meanwhile , a reliable and easy-to-obtain supervisio...
Conference Paper
Full-text available
Nonnegative matrix factorization (NMF), a well-known technique to find parts-based representations of nonnegative data, has been widely studied. In reality, ordinal relations often exist among data, such as data i is more related to j than to q. Such relative order is naturally available, and more importantly, it truly reflects the latent data stru...
Conference Paper
Full-text available
Trust prediction, aiming to predict the trust relations between users in a social network, is a key to helping users discover the reliable information. Many trust prediction methods are proposed based on the low-rank assumption of a trust network. However, one typical property of the trust network is that the trust relations follow the power-law di...
Article
Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full u...
Article
Full-text available
Non-negative matrix factorization (NMF), a method for finding parts-based representation of non-negative data, has shown remarkable competitiveness in data analysis. Given that real-world datasets are often comprised of multiple features or views which describe data from various perspectives, it is important to exploit diversity from multiple views...
Conference Paper
Full-text available
Real data are usually complex and contain various components. For example, face images have expressions and genders. Each component mainly reflects one aspect of data and provides information others do not have. Therefore, exploring the semantic information of multiple components as well as the diversity among them is of great benefit to understand...
Article
Full-text available
Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion betw...
Article
Full-text available
Non-isometric surface registration, aiming to align two surfaces with different sizes and details, has been widely used in computer animation industry. Various existing surface registration approaches have been proposed for accurate template fitting; nevertheless, two challenges remain. One is how to avoid the mesh distortion and fold over of surfa...
Article
Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, suc...
Article
Uncovering community structures is a fundamental and important problem for analyzing complex networks. The topology information, as the direct representation of networks, is widely used for community detection. But in fact, there are other two important types of information related with network topology: the global information which captures the im...
Article
Full-text available
Subspace clustering aims to partition the data points drawn from a union of subspaces according to their underlying subspaces. For accurate semisupervised subspace clustering, all data that have a must-link constraint or the same label should be grouped into the same underlying subspace. However, this is not guaranteed in existing approaches. Moreo...
Conference Paper
Full-text available
Multi-view clustering, which explores complementary information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose...
Article
Feature selection is to select a subset of relevant features from the original feature set. In practical applications, regarding the unavailability of an amount of the labels is still a challenging problem. To overcome this problem, unsupervised feature selection algorithms have been developed and achieve promising performance. However, most existi...

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