A Combined Dimensional Kernel Method for Graph Classification

2017 ◽  
Vol 10 (3) ◽  
pp. 22-33
Author(s):  
Tiejun Cao
Keyword(s):  
Structural Information ◽  
Kernel Method ◽  
Three Dimensional ◽  
Graph Classification ◽  
Effective Technique ◽  
Quadratic Constraints ◽  
Molecular Chemistry ◽  
One Dimensional ◽  
Different Dimensions ◽  
Optimal Kernel

The data containing structural information is an important problem in the field of machine learning. Kernel methods is an effective technique for solving such problems. A combined dimension kernel method is proposed or graph classification in this paper. A two-dimensional kernel is first constructed in this method, and it incorporates one-dimensional information to characterize the molecular chemistry, and then a three-dimensional kernel is constructed based on the knowledge of molecular mechanics to characterize the physical properties of the molecule. On this basis, the kernel of different dimensions is integrated, and the quadratic programming problem with quadratic constraints is solved to obtain the optimal kernel combination. The experimental results show that the proposed method has better performance than the prior technology, and it outperforms the existing algorithm.

Biogc: A novel framework for biological network classification via machine learning

2021 ◽  
Vol 25 (5) ◽  
pp. 1153-1168
Author(s):  
Bentian Li ◽  
Dechang Pi ◽  
Yunxia Lin ◽  
Izhar Ahmed Khan
Keyword(s):  
Large Scale ◽  
Biological Network ◽  
Structural Information ◽  
Kernel Method ◽  
Small Scale ◽  
Network Data ◽  
Graph Classification ◽  
Accuracy Rate ◽  
Proposed Model ◽  
Complex Structural

Biological network classification is an eminently challenging task in the domain of data mining since the networks contain complex structural information. Conventional biochemical experimental methods and the existing intelligent algorithms still suffer from some limitations such as immense experimental cost and inferior accuracy rate. To solve these problems, in this paper, we propose a novel framework for Biological graph classification named Biogc, which is specifically developed to predict the label of both small-scale and large-scale biological network data flexibly and efficiently. Our framework firstly presents a simplified graph kernel method to capture the structural information of each graph. Then, the obtained informative features are adopted to train different scale biological network data-oriented classifiers to construct the prediction model. Extensive experiments on five benchmark biological network datasets on graph classification task show that the proposed model Biogc outperforms the state-of-the-art methods with an accuracy rate of 98.90% on a larger dataset and 99.32% on a smaller dataset.

scholarly journals A Comprehensive Evaluation of Graph Kernels for Unattributed Graphs

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 984 ◽  
Author(s):  
Yi Zhang ◽  
Lulu Wang ◽  
Liandong Wang
Keyword(s):  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Evaluation Framework ◽  
Graph Classification ◽  
Open Problems ◽  
Graph Kernels ◽  
Different Dimensions ◽  
Synthetic Datasets ◽  
Optimal Kernel ◽  
Kernel Design

Graph kernels are of vital importance in the field of graph comparison and classification. However, how to compare and evaluate graph kernels and how to choose an optimal kernel for a practical classification problem remain open problems. In this paper, a comprehensive evaluation framework of graph kernels is proposed for unattributed graph classification. According to the kernel design methods, the whole graph kernel family can be categorized in five different dimensions, and then several representative graph kernels are chosen from these categories to perform the evaluation. With plenty of real-world and synthetic datasets, kernels are compared by many criteria such as classification accuracy, F1 score, runtime cost, scalability and applicability. Finally, quantitative conclusions are discussed based on the analyses of the extensive experimental results. The main contribution of this paper is that a comprehensive evaluation framework of graph kernels is proposed, which is significant for graph-classification applications and the future kernel research.

Theoretical design of MoO3-based high-rate lithium ion battery electrodes: the effect of dimensionality reduction

2014 ◽  
Vol 2 (45) ◽  
pp. 19180-19188 ◽  
Author(s):  
Fengyu Li ◽  
Carlos R. Cabrera ◽  
Zhongfang Chen
Keyword(s):  
Density Functional ◽  
Three Dimensional ◽  
Lithium Ion ◽  
High Rate ◽  
Functional Theory ◽  
One Dimensional ◽  
Theoretical Design ◽  
Different Dimensions ◽  
Li Adsorption ◽  
And Diffusion

By means of density functional theory computations, we systematically investigated the behavior of lithium (Li) adsorption and diffusion on MoO3 with different dimensions: including three-dimensional (3D) bulk, two-dimensional (2D) double-layer, 2D monolayer and one-dimensional (1D) nanoribbons.

Analysis of Pulsed Thermography Methods for Defect Depth Prediction

2005 ◽  
Vol 128 (4) ◽  
pp. 329-338 ◽  
Author(s):  
J. G. Sun
Keyword(s):  
Test Specimen ◽  
Three Dimensional ◽  
Quantitative Prediction ◽  
Defect Depth ◽  
Effective Technique ◽  
Flat Bottom ◽  
Dimensional Solution ◽  
Pulsed Thermography ◽  
One Dimensional ◽  
Depth Prediction

Pulsed thermography is an effective technique for quantitative prediction of defect depth within a specimen. Several methods have been reported in the literature. In this paper, using an analysis based on a theoretical one-dimensional solution of pulsed thermography, we analyzed four representative methods. We show that all of the methods are accurate and converge to the theoretical solution under ideal conditions. Three methods can be directly used to predict defect depth. However, because defect features that appear on the surface during a pulsed thermography test are always affected by three-dimensional heat conduction within the test specimen, the performance and accuracy of these methods differs for defects of various sizes and depths. This difference is demonstrated and evaluated from a set of pulsed thermography data obtained from a specimen with several flat-bottom holes as simulated defects.

scholarly journals Needs of all Consumer Beauty Products One- Dimensional Human Critical Study of the Consumer Beauty Products the Body Shop

2021 ◽  
pp. 37-46
Author(s):  
Eduardus Hena ◽  
Udi Rusadi ◽  
Mikhael Dua
Keyword(s):  
Digital Media ◽  
Three Dimensional ◽  
The Body ◽  
Critical Study ◽  
One Dimensional ◽  
Different Dimensions ◽  
Advertising Discourse ◽  
The Body Shop ◽  
Capitalist Ideology ◽  
Beauty Products

Beauty consumers are currently promised to satisfy their needs by interpolating capitalist ideology in The Body Shop's advertising discourse on digital media, which can eliminate Reason in fulfilling requirements. This research was conducted to examine the interpellation of capitalist ideology in exploiting consumers' beauty needs, consumers to process the interpellation of capitalist ideologies to fulfill apparent beauty needs, and the emancipation process to free these consumers. The research found that the interpellation of capitalist ideology in The Body Shop's advertising discourse on digital media to exploit consumers through preconditions of pleasure, exploitation of instincts, alienation, and sublimation of concern, sublimation for transformation, separation, and discrimination of real needs; in the way of shaking logic, manipulative messages, promising the pleasures of beauty necessities. Each consumer responds to the interpellation of capitalist ideology in different dimensions: 1) Two-dimensional consumers maintain rational and irrational reasoning to meet their real needs, 2) One-dimensional consumers only use illogical reasoning to fulfill apparent needs, 3) Consumers with metamorphosis dimensions with superficial needs. For this reason, a new size is needed in the digital communication era, namely "Three Dimensional Consumers: Combine rational reasoning, irrational reasoning, humanist communication media, so that consumers meet their true humanist and transcendent needs.

Structure and function of neural networks in the retina

1988 ◽  
Vol 46 ◽  
pp. 26-27
Author(s):  
Peter Sterling
Keyword(s):  
Ganglion Cells ◽  
Three Dimensional ◽  
Structure And Function ◽  
Synaptic Connections ◽  
Visual Axis ◽  
One Dimensional ◽  
Cat Retina ◽  
Ultrathin Sections ◽  
And Function ◽  
Insight Into

The synaptic connections in cat retina that link photoreceptors to ganglion cells have been analyzed quantitatively. Our approach has been to prepare serial, ultrathin sections and photograph en montage at low magnification (˜2000X) in the electron microscope. Six series, 100-300 sections long, have been prepared over the last decade. They derive from different cats but always from the same region of retina, about one degree from the center of the visual axis. The material has been analyzed by reconstructing adjacent neurons in each array and then identifying systematically the synaptic connections between arrays. Most reconstructions were done manually by tracing the outlines of processes in successive sections onto acetate sheets aligned on a cartoonist's jig. The tracings were then digitized, stacked by computer, and printed with the hidden lines removed. The results have provided rather than the usual one-dimensional account of pathways, a three-dimensional account of circuits. From this has emerged insight into the functional architecture.

Two-Dimensional Crystallization of Tetanus Toxin

1985 ◽  
Vol 43 ◽  
pp. 528-529
Author(s):  
Jeffry A. Reidler ◽  
John P. Robinson
Keyword(s):  
Electron Microscopy ◽  
Tetanus Toxin ◽  
Structural Information ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Membrane Interface ◽  
3D Reconstructions ◽  
Cellular Receptors ◽  
Computer Reconstruction ◽  
2D Crystals

We have prepared two-dimensional (2D) crystals of tetanus toxin using procedures developed by Uzgiris and Kornberg for the directed production of 2D crystals of monoclonal antibodies at an antigen-phospholipid monolayer interface. The tetanus toxin crystals were formed using a small mole fraction of the natural receptor, GT1, incorporated into phosphatidyl choline monolayers. The crystals formed at low concentration overnight. Two dimensional crystals of this type are particularly useful for structure determination using electron microscopy and computer image refinement. Three dimensional (3D) structural information can be derived from these crystals by computer reconstruction of photographs of toxin crystals taken at different tilt angles. Such 3D reconstructions may help elucidate the mechanism of entry of the enzymatic subunit of toxins into cells, particularly since these crystals form directly on a membrane interface at similar concentrations of ganglioside GT1 to the natural cellular receptors.

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

Exploring the Multidimensional Facets of Authoritarianism: Authoritarian Aggression and Social Dominance Orientation

2008 ◽  
Vol 67 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Stefano Passini
Keyword(s):  
Social Dominance ◽  
Factor Model ◽  
Three Dimensional ◽  
Social Dominance Orientation ◽  
Model Fit ◽  
Regression Analyses ◽  
One Dimensional ◽  
Confirmatory Factor ◽  
The One ◽  
Better Than

The relation between authoritarianism and social dominance orientation was analyzed, with authoritarianism measured using a three-dimensional scale. The implicit multidimensional structure (authoritarian submission, conventionalism, authoritarian aggression) of Altemeyer’s (1981, 1988) conceptualization of authoritarianism is inconsistent with its one-dimensional methodological operationalization. The dimensionality of authoritarianism was investigated using confirmatory factor analysis in a sample of 713 university students. As hypothesized, the three-factor model fit the data significantly better than the one-factor model. Regression analyses revealed that only authoritarian aggression was related to social dominance orientation. That is, only intolerance of deviance was related to high social dominance, whereas submissiveness was not.

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