scholarly journals DOPSIE: Deep-Order Proximity and Structural Information Embedding

2019 ◽  
Vol 1 (2) ◽  
pp. 684-697
Author(s):  
Mario Manzo ◽  
Alessandro Rozza
Keyword(s):  
Vector Space ◽  
Structural Information ◽  
Graph Embedding ◽  
Depth Information ◽  
Classification Problems ◽  
Characteristic Path Length ◽  
Topological Information ◽  
Neighborhood Information ◽  
Information Embedding ◽  
Optimal Network

Graph-embedding algorithms map a graph into a vector space with the aim of preserving its structure and its intrinsic properties. Unfortunately, many of them are not able to encode the neighborhood information of the nodes well, especially from a topological prospective. To address this limitation, we propose a novel graph-embedding method called Deep-Order Proximity and Structural Information Embedding (DOPSIE). It provides topology and depth information at the same time through the analysis of the graph structure. Topological information is provided through clustering coefficients (CCs), which is connected to other structural properties, such as transitivity, density, characteristic path length, and efficiency, useful for representation in the vector space. The combination of individual node properties and neighborhood information constitutes an optimal network representation. Our experimental results show that DOPSIE outperforms state-of-the-art embedding methodologies in different classification problems.

scholarly journals Hierarchical stochastic graphlet embedding for graph-based pattern recognition

2019 ◽  
Vol 32 (15) ◽  
pp. 11579-11596
Author(s):  
Anjan Dutta ◽  
Pau Riba ◽  
Josep Lladós ◽  
Alicia Fornés
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Vector Space ◽  
Hierarchical Structure ◽  
Structural Information ◽  
Graph Embedding ◽  
Graph Representation ◽  
Machine Learning Techniques ◽  
Hierarchical Level ◽  
The Hierarchical Structure

AbstractDespite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the stochastic graphlet embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low-to-high-order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods.

scholarly journals Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang-Rui Ren ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Junliang Shang ◽  
Chun-Hou Zheng
Keyword(s):  
Extreme Learning Machine ◽  
High Performance ◽  
Structural Information ◽  
Optimization Method ◽  
Classification Performance ◽  
Weight Matrix ◽  
New Method ◽  
Classification Problems ◽  
Graph Regularization ◽  
Learning Machine

Abstract Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

scholarly journals Hierarchical Multi Twin Support Vector Machine

2021 ◽  
Vol 130 (2B) ◽  
Author(s):  
Nguyen The Cuong
Keyword(s):  
Support Vector Machine ◽  
Structural Information ◽  
Binary Classification ◽  
Classification Problem ◽  
Data Type ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Data Simulation ◽  
Better Than

In binary classification problems, two classes normally have different tendencies. More complex, the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) don't sufficiently exploit structural information with cluster granularity of the data, cause of restricts the capability of simulation of data trends. Structural twin support vector machine (S-TWSVM) sufficiently exploits structural information with cluster granularity of one class for learning a represented hyperplane of that class. This makes S-TWSVM's data simulation capabilities better than TWSVM. However, for the data type that each class consists of clusters of different trends, the capability of simulation of S-TWSVM is restricted. In this paper, we propose a new Hierarchical Multi Twin Support Vector Machine (called HM-TWSVM) for classification problem with each cluster-vs-class strategy. HM-TWSVM overcomes the limitations of S-TWSVM. Experiment results show that HM-TWSVM could describe the tendency of each cluster.

Hierarchical graph embedding in vector space by graph pyramid

2017 ◽  
Vol 61 ◽  
pp. 245-254 ◽  
Author(s):  
Seyedeh Fatemeh Mousavi ◽  
Mehran Safayani ◽  
Abdolreza Mirzaei ◽  
Hoda Bahonar
Keyword(s):  
Vector Space ◽  
Graph Embedding ◽  
Hierarchical Graph

scholarly journals Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat

2020 ◽  
Vol 12 (24) ◽  
pp. 4040
Author(s):  
Ke Xu ◽  
Jingchao Zhang ◽  
Huaimin Li ◽  
Weixing Cao ◽  
Yan Zhu ◽  
...  
Keyword(s):  
Structural Information ◽  
Spectral Characteristics ◽  
Canopy Structure ◽  
Growth Period ◽  
Accurate Estimation ◽  
Depth Information ◽  
Nitrogen Accumulation ◽  
Hole Filling ◽  
Depth Images ◽  
Mean Square Errors

The accurate estimation of nitrogen accumulation is of great significance to nitrogen fertilizer management in wheat production. To overcome the shortcomings of spectral technology, which ignores the anisotropy of canopy structure when predicting the nitrogen accumulation in wheat, resulting in low accuracy and unstable prediction results, we propose a method for predicting wheat nitrogen accumulation based on the fusion of spectral and canopy structure features. After depth images are repaired using a hole-filling algorithm, RGB images and depth images are fused through IHS transformation, and textural features of the fused images are then extracted in order to express the three-dimensional structural information of the canopy. The fused images contain depth information of the canopy, which breaks through the limitation of extracting canopy structure features from a two-dimensional image. By comparing the experimental results of multiple regression analyses and BP neural networks, we found that the characteristics of the canopy structure effectively compensated for the model prediction of nitrogen accumulation based only on spectral characteristics. Our prediction model displayed better accuracy and stability, with prediction accuracy values (R2) based on BP neural network for the leaf layer nitrogen accumulation (LNA) and shoot nitrogen accumulation (SNA) during a full growth period of 0.74 and 0.73, respectively, and corresponding relative root mean square errors (RRMSEs) of 40.13% and 35.73%.

scholarly journals Simultaneous Dual-Modal Multispectral Photoacoustic and Ultrasound Macroscopy for Three-Dimensional Whole-Body Imaging of Small Animals

Photonics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 13
Author(s):  
Eun-Yeong Park ◽  
Sinyoung Park ◽  
Haeni Lee ◽  
Munsik Kang ◽  
Chulhong Kim ◽  
...  
Keyword(s):  
Ultrasound Imaging ◽  
Biological Tissue ◽  
Structural Information ◽  
Imaging Technique ◽  
Photoacoustic Imaging ◽  
Biological Tissues ◽  
Whole Body ◽  
Depth Information ◽  
Whole Body Imaging ◽  
Ultrasound Images

Photoacoustic imaging is a promising medical imaging technique that provides excellent function imaging of an underlying biological tissue or organ. However, it is limited in providing structural information compared to other imaging modalities, such as ultrasound imaging. Thus, to offer complete morphological details of biological tissues, photoacoustic imaging is typically integrated with ultrasound imaging. This dual-modal imaging technique is already implemented on commercial clinical ultrasound imaging platforms. However, commercial platforms suffer from limited elevation resolution compared to the lateral and axial resolution. We have successfully developed a dual-modal photoacoustic and ultrasound imaging to address these limitations, specifically targeting animal studies. The system can acquire whole-body images of mice in vivo and provide complementary structural and functional information of biological tissue information simultaneously. The color-coded depth information can be readily obtained in photoacoustic images using complementary information from ultrasound images. The system can be used for several biomedical applications, including drug delivery, biodistribution assessment, and agent testing.

scholarly journals A Self-Improving Framework for Joint Depth Estimation and Underwater Target Detection from Hyperspectral Imagery

2021 ◽  
Vol 13 (9) ◽  
pp. 1721
Author(s):  
Jiahao Qi ◽  
Pengcheng Wan ◽  
Zhiqiang Gong ◽  
Wei Xue ◽  
Aihuan Yao ◽  
...  
Keyword(s):  
Target Detection ◽  
Structural Information ◽  
Hyperspectral Imagery ◽  
Contextual Information ◽  
Water Environment ◽  
Depth Estimation ◽  
Detection Methods ◽  
Depth Information ◽  
Underwater Target Detection ◽  
Underwater Target

Underwater target detection (UTD) is one of the most attractive research topics in hyperspectral imagery (HSI) processing. Most of the existing methods are presented to predict the signatures of desired targets in an underwater context but ignore the depth information which is position-sensitive and contributes significantly to distinguishing the background and target pixels. So as to take full advantage of the depth information, in this paper a self-improving framework is proposed to perform joint depth estimation and underwater target detection, which exploits the depth information and detection results to alternately boost the final detection performance. However, it is difficult to calculate depth information under the interference of a water environment. To address this dilemma, the proposed framework, named self-improving underwater target detection framework (SUTDF), employs the spectral and spatial contextual information to pick out target-associated pixels as the guidance dataset for depth estimation work. Considering the incompleteness of the guidance dataset, an expectation-maximum liked updating scheme has also been developed to iteratively excavate the statistical and structural information from input HSI for further improving the diversity of the guidance dataset. During each updating epoch, the calculated depth information is used to yield a more diversified dataset for the target detection network, leading to a more accurate detection result. Meanwhile, the detection result will in turn contribute in detecting more target-associated pixels as the supplement for the guidance dataset, eventually promoting the capacity of the depth estimation network. With this specific self-improving framework, we can provide a more precise detection result for a hyperspectral UTD task. Qualitative and quantitative illustrations verify the effectiveness and efficiency of SUTDF in comparison with state-of-the-art underwater target detection methods.

scholarly journals Comparative Analysis of Unsupervised Protein Similarity Prediction Based on Graph Embedding

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Ziqi Wang ◽  
Shudong Wang ◽  
Junliang Shang
Keyword(s):  
Link Prediction ◽  
Structural Information ◽  
Graph Embedding ◽  
Go Annotation ◽  
Protein Protein Interaction ◽  
Protein Functions ◽  
Cell Components ◽  
Protein Similarity Networks ◽  
Go Terms ◽  
Embedding Methods

The study of protein–protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardized terms that describe gene products from molecular functions, biological processes, and cell components. Previous studies on assessing the similarity of GO terms were primarily based on Information Content (IC) between GO terms to measure the similarity of proteins. However, these methods tend to ignore the structural information between GO terms. Therefore, considering the structural information of GO terms, we systematically analyze the performance of the GO graph and GO Annotation (GOA) graph in calculating the similarity of proteins using different graph embedding methods. When applied to the actual Human and Yeast datasets, the feature vectors of GO terms and proteins are learned based on different graph embedding methods. To measure the similarity of the proteins annotated by different GO numbers, we used Dynamic Time Warping (DTW) and cosine to calculate protein similarity in GO graph and GOA graph, respectively. Link prediction experiments were then performed to evaluate the reliability of protein similarity networks constructed by different methods. It is shown that graph embedding methods have obvious advantages over the traditional IC-based methods. We found that random walk graph embedding methods, in particular, showed excellent performance in calculating the similarity of proteins. By comparing link prediction experiment results from GO(DTW) and GOA(cosine) methods, it is shown that GO(DTW) features provide highly effective information for analyzing the similarity among proteins.

scholarly journals WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE

2021 ◽  
Vol 37 (1) ◽  
pp. 43-56
Author(s):  
Nguyen The Cuong ◽  
Huynh The Phung
Keyword(s):  
Support Vector Machine ◽  
Structural Information ◽  
Binary Classification ◽  
Classification Problem ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Data Simulation ◽  
Training Time ◽  
Structural Support

In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation on the capability of simulation of data trends. Structural Twin Support Vector Machine (S-TWSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Therefore, the capability of S-TWSVM’s data simulation is better than that of TWSVM. However, for the datasets where each class consists of clusters of different trends, the S-TWSVM’s data simulation capability seems restricted. Besides, the training time of S-TWSVM has not been improved compared to TWSVM. This paper proposes a new Weighted Structural - Support Vector Machine (called WS-SVM) for binary classification problems with a class-vs-clusters strategy. Experimental results show that WS-SVM could describe the tendency of the distribution of cluster information. Furthermore, both the theory and experiment show that the training time of the WS-SVM for classification problem has significantly improved compared to S-TWSVM.

Sign in / Sign up

Export Citation Format

Share Document