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共 39 条记录，以下是21-30 订阅

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graph-based vs. Vector-based Classification: A Fair Comparison 13th

Graph-Based vs. Vector-Based Classification: A Fair Comparis...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Gillioz, Anthony Riesen, Kaspar Institute of Computer Science University of Bern Bern Switzerland Institute for Information Systems University of Applied Science and Arts Northwestern Switzerland Olten Switzerland

ISBN: (纸本)9783031427947

Numerous graph classifiers are readily available and frequently used in both research and industry. Ensuring their performance across multiple domains and applications is crucial. In this paper, we conduct a comprehensive assessment of three commonly used graph-based classifiers across 24 graph datasets (we employ classifiers based on graph matchings, graph kernels, and graph neural networks). Our goal is to find out what primarily affects the performance of these classifiers in different tasks. To this end, we compare each of the three classifiers in three different scenarios. In the first scenario, the classifier has access to the original graphs, in the second scenario, the same classifier has access only to the structure of the graph (without labels), and in the third scenario, we replace the graph-based classifiers with a corresponding related statistical classifier, which has access only to an aggregated feature vector of the graph labels. On the basis of this exhaustive evaluation, we are able to suggest whether or not certain graph datasets are suitable for specific benchmark comparisons. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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4th international workshop on Energy Minimization Methods in Computer Vision and pattern recognition, EMMCVPR 2003

4th International Workshop on Energy Minimization Methods in...

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4th international workshop on Energy Minimization Methods in Computer Vision and pattern recognition, EMMCVPR 2003

ISBN: (纸本)3540404988

the proceedings contain 39 papers. the special focus in this conference is on Unsupervised Learning, Matching, Probabilistic Modelling, Segmentation, Grouping, Shape Modelling and Reconstruction. the topics include: Stochastic search for optimal linear representations of images on spaces with orthogonality constraints;curve matching using the fast marching method;EM algorithm for clustering an ensemble of graphs with comb matching;information force clustering using directed trees;active sampling strategies for multihypothesis testing;likelihood based hierarchical clustering and network topology identification;learning mixtures of tree-unions by minimizing description length;hierarchical annealing for random image synthesis;semi-supervised image segmentation by parametric distributional clustering;path variation and image segmentation;a fast snake segmentation method applied to histopathological sections;a compositionality architecture for perceptual feature grouping;using prior shape and points in medical image segmentation;separating a texture from an arbitrary background using pair wise grey level cooccurrences;surface recovery from 3D point data using a combined parametric and geometric flow approach;curvature vector flow to assure convergent deformable models for shape modelling;a MAP estimation algorithm using IIR recursive filters;estimation of rank deficient matrices from partial observations;contextual and non-combinatorial approach to feature extraction;generalized multi-camera scene reconstruction using graph cuts and graph matching using spectral seriation.

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An Efficient Entropy-based graph Kernel 13th

An Efficient Entropy-Based Graph Kernel

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Ourdjini, Aymen Kiouche, Abd Errahmane Seba, Hamida Alger Oued Smar Algeria Université de Lyon Université Lyon 1 LIRIS UMR 5205 Lyon69622 France

ISBN: (纸本)9783031427947

graph kernels are methods used in machine learning algorithms for handling graph-structured data. they are widely used for graph classification in various domains and are particularly valued for their accuracy. However, most existing graph kernels are not fast enough. To address this issue, we propose a new graph kernel based on the concept of entropy. Our method has the advantage of handling labeled and attributed graphs while significantly reducing computation time when compared to other graph kernels. We evaluated our method on several datasets and compared it with various state-of-the-art techniques. the results show a clear improvement in the performance of the initial method. Furthermore, our findings rank among the best in terms of classification accuracy and computation speed compared to other graph kernels. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

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Quadratic Kernel Learning for Interpolation Kernel Machine based graph Classification 13th

Quadratic Kernel Learning for Interpolation Kernel Machine ...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Zhang, Jiaqi Liu, Cheng-Lin Jiang, Xiaoyi Faculty of Mathematics and Computer Science University of Münster Einsteinstrasse 62 Münster48149 Germany National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing10049 China

ISBN: (纸本)9783031427947

Interpolating classifiers interpolate all the training data and thus have zero training error. Recent research shows their fundamental importance for high-performance ensemble techniques. Interpolation kernel machines belong to the class of interpolating classifiers and do generalize well. they have been demonstrated to be a good alternative to support vector machine for graph classification. In this work we further improve their performance by considering multiple kernel learning. We establish a general scheme for achieving this goal. the current experimental work is done using quadratic kernel combination. Our experimental results demonstrate the performance boosting potential of our approach against the use of individual graph kernels. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Swap strategies for graph matching

Swap strategies for graph matching

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4th international workshop on graph based representations in pattern recognition

作者： Fosser, P Glantz, R Locatelli, M Pelillo, M Univ Ca Foscari Venezia Dipartimento Informat I-30172 Venice Italy Univ Turin Dipartimento Informat I-10149 Turin Italy

ISBN: (纸本)354040452X

the problem of graph matching is usually approached via explicit search in state-space or via energy minimization. In this paper we deal with a class of heuristics coming from a combination of both approaches. Combinatorially, the basic heuristic of the class can be interpreted as a greedy algorithm to form maximal cliques in an association graph. To avoid one of the main drawbacks of greedy strategies, i.e. that they are easily fooled by poor local optima, we propose a modification which allows for vertex swaps during the formation of a clique. Experiments on random graphs show the effectiveness of the proposed heuristics both in terms of quality of solutions and speed.

关键词： Artificial intelligence

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Comparison of distance measures for graph-based clustering of documents

Comparison of distance measures for graph-based clustering o...

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4th international workshop on graph based representations in pattern recognition

作者： Schenker, A Last, M Bunke, H Kandel, A Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Ben Gurion Univ Negev Dept Informat Syst Engn IL-84105 Beer Sheva Israel Univ Bern Dept Comp Sci CH-3012 Bern Switzerland

ISBN: (纸本)354040452X

In this paper we describe work relating to clustering of document collections. We compare the conventional vector-model approach using cosine similarity and Euclidean distance to a novel method we have developed for clustering graph-based data with the standard k-means algorithm. the proposed method is evaluated using five different graph distance measures under three clustering performance indices. the experiments are performed on two separate document collections. the results show the graph-based approach performs as well as vector-based methods or even better when using normalized graph distance measures.

关键词： graphic methods

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Splitting Structural and Semantic Knowledge in graph Autoencoders for graph Regression 13th

Splitting Structural and Semantic Knowledge in Graph Autoe...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Fadlallah, Sarah Segura Alabart, Natália Julià, Carme Serratosa, Francesc Research Group ASCLEPIUS: Smart Technology for Smart Healthcare Department D’Enginyeria Informática I Matemátiques Universitat Rovira I Virgili Catalonia Tarragona43k007 Spain

ISBN: (纸本)9783031427947

this paper introduces ReGengraph, a new method for graph regression that combines two well-known modules: an autoencoder and a graph autoencoder. the main objective of our proposal is to split the knowledge in the graph nodes into semantic and structural knowledge during the embedding process. It uses the autoencoder to extract the semantic knowledge and the graph autoencoder to extract the structural knowledge. the resulting embedded vectors of both modules are then combined and used for graph regression to predict a global property of the graph. the method demonstrates improved performance compared to classical methods, i.e., autoencoders or graph autoencoders alone. the approach has been applied to predict the binding energy of chemical compounds represented as attributed graphs but could be used in other fields as well. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Binding energy

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graph-based analysis of nasopharyngeal carcinoma with bayesian network learning methods

Graph-based analysis of nasopharyngeal carcinoma with bayesi...

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7th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2009

作者： Aussem, Alex Rodrigues De Morais, Sergio Corbex, Marilys Favrel, Joël University of Lyon LIESP Université de Lyon 1 Villeurbanne F-69622 France 150 cours Albert Thomas Lyon Cedex 08 F-69280 France

ISBN: (纸本)3642021239

In this paper, we propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of Nasopharyngeal Carcinoma (NPC) based on a case-control epidemiologic study. this framework builds on the use of Bayesian network for representing statistical dependencies between the random variables. BN is directed acyclic graphs that models the joint multivariate probability distribution underlying the data. these graphical models are highly attractive for their ability to describe complex probabilistic interactions between variables. the graph provides a statistical profile of the recruited population and meanwhile help identify a subset of features that are most relevant for probabilistic classification of the NPC. We report experiment results with the NPC case-study data using a novel constraint-based BN structure learning algorithm. We show how the DAG provides an improved comprehension of NPC etiology. Our findings are compared with the risk factors that were suggested in the recent literature in cancerology. © 2009 Springer Berlin Heidelberg.

关键词： Bayesian networks

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Maximal Independent Sets for Pooling in graph Neural Networks 13th

Maximal Independent Sets for Pooling in Graph Neural Netwo...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Stanovic, Stevan Gaüzère, Benoit Brun, Luc Normandie Univ ENSICAEN CNRS UNICAEN GREYC UMR 6072 Caen14000 France INSA Rouen Normandie Univ Rouen Normandie Université Le Havre Normandie Normandie Univ LITIS UR 4108 Rouen76000 France

ISBN: (纸本)9783031427947

Convolutional Neural Networks (CNNs) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete lattice into a reduced lattice with the same connectivity and allows reduction functions to consider all pixels in an image. However, there is no pooling that satisfies these properties for graphs. In fact, traditional graph pooling methods suffer from at least one of the following drawbacks: graph disconnection or overconnection, low decimation ratio, and deletion of large parts of graphs. In this paper, we present three pooling methods based on the notion of maximal independent sets that avoid these pitfalls. Our experimental results confirm the relevance of maximal independent set constraints for graph pooling. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： graph neural networks

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Minimum Spanning Set Selection in graph Kernels 13th

Minimum Spanning Set Selection in Graph Kernels

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Tortorella, Domenico Micheli, Alessio Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy

ISBN: (纸本)9783031427947

Kernel-based learning models such as support vector machines (SVMs) can seamlessly deal with graph structures thanks to suitable kernel functions that compute a particular similarity between pairs of data samples. In the last two decades, a plethora of graph kernels have been proposed, motivated by theoretical properties or designed specifically for an application domain. Computing graph kernels however presents a significant cost for both training and inference, since predictions on unseen graphs require evaluating the kernel e.g. between the new sample and all support vectors, and solutions to an SVM optimization problem are not guaranteed to be sparse. In this paper, we present a method to select a minimum set of spanning vectors for the solutions of SVMs, based on the rank-revealing QR decomposition of the kernel Gram matrix. We apply it on 18 real-world classification tasks on chemical compounds, showing its effectiveness to reduce the computational burden in performing inference on unseen graphs by minimizing the number of support vectors without penalizing accuracy. this in turn gives us a tool to better analyze the trade-off between accuracy, expressiveness and inference cost among different graph kernels. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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