scholarly journals Category-Oriented Self-Learning Graph Embedding for Efficient Image Compact Representation

2021 ◽  
Author(s):  
Liangchen Hu
Keyword(s):  
Graph Embedding ◽  
Global Perspective ◽  
Compact Representation ◽  
Structure Information ◽  
Learning Framework ◽  
Class Separability ◽  
Linear Classifier ◽  
Label Information ◽  
Low Dimensional ◽  
Self Learning

As one of the ways to acquire efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are intended to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within the class but excluding the affinity of different classes, which results in the inability to fully capture the marginal structure between distributions of different classes. To overcome these issues, we propose a learning framework that implements Category-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can easily be extended to the semi-supervised situation. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method comparing with some state-of-the-art approaches.

scholarly journals Category-Oriented Self-Learning Graph Embedding for Efficient Image Compact Representation

2021 ◽  
Author(s):  
Liangchen Hu
Keyword(s):  
Graph Embedding ◽  
Global Perspective ◽  
Compact Representation ◽  
Structure Information ◽  
Learning Framework ◽  
Class Separability ◽  
Linear Classifier ◽  
Label Information ◽  
Low Dimensional ◽  
Self Learning

As one of the ways to acquire efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are intended to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within the class but excluding the affinity of different classes, which results in the inability to fully capture the marginal structure between distributions of different classes. To overcome these issues, we propose a learning framework that implements Category-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can easily be extended to the semi-supervised situation. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method comparing with some state-of-the-art approaches.

scholarly journals An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1767
Author(s):  
Xin Xu ◽  
Yang Lu ◽  
Yupeng Zhou ◽  
Zhiguo Fu ◽  
Yanjie Fu ◽  
...  
Keyword(s):  
Random Walk ◽  
Representation Learning ◽  
Local Information ◽  
Learning Framework ◽  
Network Representation ◽  
Label Node ◽  
Label Information ◽  
Classification Tasks ◽  
Node Classification ◽  
Low Dimensional

Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network representation learning methods have been proposed, where random walk strategy is one of the wildly utilized approaches. However, the existing random walk based methods have some challenges, including: 1. The insufficiency of explaining what network knowledge in the walking path-samplings; 2. The adverse effects caused by the mixture of different information in networks; 3. The poor generality of the methods with hyper-parameters on different networks. This paper proposes an information-explainable random walk based unsupervised network representation learning framework named Probabilistic Accepted Walk (PAW) to obtain network representation from the perspective of the stationary distribution of networks. In the framework, we design two stationary distributions based on nodes’ self-information and local-information of networks to guide our proposed random walk strategy to learn representational vectors of networks through sampling paths of nodes. Numerous experimental results demonstrated that the PAW could obtain more expressive representation than the other six widely used unsupervised network representation learning baselines on four real-world networks in single-label and multi-label node classification tasks.

scholarly journals PolSAR Image Feature Extraction via Co-Regularized Graph Embedding

2020 ◽  
Vol 12 (11) ◽  
pp. 1738
Author(s):  
Xiayuan Huang ◽  
Xiangli Nie ◽  
Hong Qiao
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Weighted Graph ◽  
Graph Embedding ◽  
Image Feature ◽  
Weighted Graphs ◽  
Image Feature Extraction ◽  
Structure Information ◽  
Low Dimensional ◽  
Low Dimensional Features

Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information. Moreover, the similarity measurement is a crucial factor for constructing a weighted graph. Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (PolSAR) image classification. For obtaining a satisfactory PolSAR image classification performance, a co-regularized graph embedding (CRGE) method by combing the two distances is proposed for PolSAR image feature extraction in this paper. Firstly, two weighted graphs are constructed based on the two distances to represent the data’s local structure information. Specifically, the neighbouring samples are sought in a local patch to decrease computation cost and use spatial information. Next the DR model is constructed based on the two weighted graphs and co-regularization. The co-regularization aims to minimize the dissimilarity of low-dimensional features corresponding to two weighted graphs. We employ two types of co-regularization and the corresponding algorithms are proposed. Ultimately, the obtained low-dimensional features are used for PolSAR image classification. Experiments are implemented on three PolSAR datasets and results show that the co-regularized graph embedding can enhance the performance of PolSAR image classification.

scholarly journals Persona2vec: a flexible multi-role representations learning framework for graphs

2021 ◽  
Vol 7 ◽  
pp. e439
Author(s):  
Jisung Yoon ◽  
Kai-Cheng Yang ◽  
Woo-Sung Jung ◽  
Yong-Yeol Ahn
Keyword(s):  
Community Structure ◽  
Link Prediction ◽  
Graph Mining ◽  
State Of The Art ◽  
Multiple Representations ◽  
Graph Embedding ◽  
Learning Framework ◽  
Overlapping Community ◽  
Art Performance ◽  
Low Dimensional

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.

scholarly journals Adversarially Regularized Graph Autoencoder for Graph Embedding

2018 ◽  
Author(s):  
Shirui Pan ◽  
Ruiqi Hu ◽  
Guodong Long ◽  
Jing Jiang ◽  
Lina Yao ◽  
...  
Keyword(s):  
Real World ◽  
Topological Structure ◽  
Dimensional Space ◽  
Experimental Studies ◽  
Graph Embedding ◽  
Compact Representation ◽  
Graph Visualization ◽  
Graph Data ◽  
Training Scheme ◽  
Low Dimensional

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.  Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data,  but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in  real-world  graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding,  two variants of adversarial approaches,  adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction,  graph clustering, and graph visualization tasks.

scholarly journals Enhanced Unsupervised Graph Embedding via Hierarchical Graph Convolution Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
H. Zhang ◽  
J. J. Zhou ◽  
R. Li
Keyword(s):  
Graph Embedding ◽  
Network Node ◽  
Dimensional Representation ◽  
Undirected Graphs ◽  
Node Label ◽  
Structure Information ◽  
Initial Node ◽  
Hierarchical Graph ◽  
Node Classification ◽  
Low Dimensional

Graph embedding aims to learn the low-dimensional representation of nodes in the network, which has been paid more and more attention in many graph-based tasks recently. Graph Convolution Network (GCN) is a typical deep semisupervised graph embedding model, which can acquire node representation from the complex network. However, GCN usually needs to use a lot of labeled data and additional expressive features in the graph embedding learning process, so the model cannot be effectively applied to undirected graphs with only network structure information. In this paper, we propose a novel unsupervised graph embedding method via hierarchical graph convolution network (HGCN). Firstly, HGCN builds the initial node embedding and pseudo-labels for the undirected graphs, and then further uses GCNs to learn the node embedding and update labels, finally combines HGCN output representation with the initial embedding to get the graph embedding. Furthermore, we improve the model to match the different undirected networks according to the number of network node label types. Comprehensive experiments demonstrate that our proposed HGCN and HGCN∗ can significantly enhance the performance of the node classification task.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals Non-negative sparse Laplacian regularized latent multi-view subspace clustering

2021 ◽  
Vol 15 ◽  
pp. 174830262110249
Author(s):  
Cong-Zhe You ◽  
Zhen-Qiu Shu ◽  
Hong-Hui Fan
Keyword(s):  
Subspace Clustering ◽  
Structural Features ◽  
Space Representation ◽  
Graph Regularization ◽  
Structure Information ◽  
Cluster Data ◽  
Latent Space ◽  
Low Dimensional ◽  
Relationship Of ◽  
The Relationship

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.

scholarly journals Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

2021 ◽  
Author(s):  
Rogini Runghen ◽  
Daniel B Stouffer ◽  
Giulio Valentino Dalla Riva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Link Prediction ◽  
Graph Embedding ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Large Networks ◽  
Data Set ◽  
Learning Techniques ◽  
Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

scholarly journals Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes

2020 ◽  
Author(s):  
Lixin Fan ◽  
Kam Woh Ng ◽  
Ce Ju ◽  
Tianyu Zhang ◽  
Chee Seng Chan
Keyword(s):  
Large Deviations ◽  
Hamming Distance ◽  
State Of The Art ◽  
Metric Learning ◽  
Binary Codes ◽  
Distance Metric Learning ◽  
Learning Framework ◽  
Label Information ◽  
Learning To Hash ◽  
Random Codes

This paper proposes a novel deep polarized network (DPN) for learning to hash, in which each channel in the network outputs is pushed far away from zero by employing a differentiable bit-wise hinge-like loss which is dubbed as polarization loss. Reformulated within a generic Hamming Distance Metric Learning framework [Norouzi et al., 2012], the proposed polarization loss bypasses the requirement to prepare pairwise labels for (dis-)similar items and, yet, the proposed loss strictly bounds from above the pairwise Hamming Distance based losses. The intrinsic connection between pairwise and pointwise label information, as disclosed in this paper, brings about the following methodological improvements: (a) we may directly employ the proposed differentiable polarization loss with no large deviations incurred from the target Hamming distance based loss; and (b) the subtask of assigning binary codes becomes extremely simple --- even random codes assigned to each class suffice to result in state-of-the-art performances, as demonstrated in CIFAR10, NUS-WIDE and ImageNet100 datasets.

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