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Data-driven Latent graph Structure Learning for Diagnosis of Alzheimer's Syndrome 26

Data-driven Latent Graph Structure Learning for Diagnosis of...

26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Wang, Jianjia Wu, Chong Shanghai Univ Shanghai Inst Adv Commun & Data Sci Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Complex systems often have a latent graph structure. Studying the underlying graph structure will help us to analyze the mechanisms of complex phenomena. However, it is a challenging problem to learn effective graph structures from the data and apply them to downstream tasks. In this paper, we propose an end-to-end graph learning approach for Alzheimer's syndrome diagnosis based on functional magnetic resonance imaging (fMRI) data of brain regions, which is completely data-driven. the interactions between time-series of each brain region are represented as graph structures, and a multi-head attention mechanism is used to update the representations of the nodes. then, the graph structures are obtained from the feature sampling of the edges. Finally, the learned graph structure is combined with the left-out time-series data features and the node prior to completing the classification task of the brain network. In comparison with the latest research methods, our approach achieves higher classification accuracy.

关键词： Image edge detection Functional magnetic resonance imaging pattern recognition Task analysis Alzheimer's disease Complex systems

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Proceedings of the 6th international workshop on pattern recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006

Proceedings of the 6th International Workshop on Pattern Rec...

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6th international workshop on pattern recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006

ISBN: (纸本)9728865554

the proceedings contain 19 papers. the topics discussed include: peer-to-peer clustering for semantic overlay network generation;learning with general proximity measures;face segregation and recognition by cortical multi-scale line and edge coding;large scale face recognition with kernel correlation feature analysis with support vector machines;tracking and prediction of tumor movement in the abdomen;improved singular value decomposition for supervised learning in a high dimensional dataset;multi-modal categorization of medical images using texture-based symbolic representations;prediction of protein tertiary structure class from synchrotron radiation circular dichroism spectra;face recognition in different subspaces: a comparative study;facial feature tracking and occlusion recovery in American sign language;and multinomial mixture modelling for bilingual text classification.

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Irregular graph Pyramids and Representative Cocycles of Cohomology Generators

Irregular Graph Pyramids and Representative Cocycles of Coho...

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7th international workshop on graph-based representations in pattern recognition

作者： Gonzalez-Diaz, Rocio Ion, Adrian Iglesias-Ham, Mabel Kropatsch, Walter G. Univ Seville Dept Appl Math Seville Spain CENATAV Pattern Recognit Dept Havana Cuba Vienna Univ Technol Pattern Recognit & Image Proc Grp A-1040 Vienna Austria

ISBN: (纸本)9783642021237

Structural pattern recognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Cohomology can provide more refined algebraic invariants to a topological space that does homology. It assigns 'quantities' to the chains used in homology to characterize holes of any dimension. graph pyramids can be used to describe subdivisions of the same object at multiple levels of detail. this paper presents cohomology in the context of structural pattern recognition and introduces an algorithm to efficiency compute representative cocycles (the basic elements of cohomology) in 2D using a graph pyramid. Extension to nD and application in the context of pattern recognition are discussed.

关键词： graph pyramids representative cocycles of cohomology generators

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On Computing Canonical Subsets of graph-based Behavioral representations

On Computing Canonical Subsets of Graph-Based Behavioral Rep...

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7th international workshop on graph-based representations in pattern recognition

作者： Mankowski, Walter C. Bogunovich, Peter Shokoufandeh, Ali Salvucci, Dario D. Drexel Univ Dept Comp Sci Philadelphia PA 19104 USA

ISBN: (纸本)9783642021237

the collection of behavior protocols is a common practice in human factors research, but the analysis of these large data sets has always been a tedious and time-consuming process. We are interested in automatically finding canonical behaviors: a small subset of behavioral protocols that is most representative of the full data set, providing a view of the data with as few protocols as possible. Behavior protocols often have a natural graph-based representation, yet there has been little work applying graph theory to their study. In this paper we extend our recent algorithm by taking into account the graph topology induced by the paths taken through the space of possible behaviors. We applied this technique to find canonical web-browsing behaviors for computer users. By comparing identified canonical sets to a ground truth determined by expert human coders. we found that this graph-based metric outperforms our previous metric based on edit distance.

关键词： graph theory

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Optimum-Path Forest based on k-connectivity: theory and applications

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pattern recognition LETTERS 2017年第Feb.1期87卷 117-126页

作者： Papa, Joao Paulo Nachif Fernandes, Silas Evandro Falcao, Alexandre Xavier Sao Paulo State Univ Dept Comp Av Eng Luiz Edmundo Carrijo Coube BR-17033360 Bauru SP Brazil Univ Fed Sao Carlos Dept Comp Rod Washington LuisKm 235 BR-13565905 Sao Carlos SP Brazil Univ Estadual Campinas Inst Comp Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil

graph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. the experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets. (C) 2016 Elsevier B.V. All rights reserved.

关键词： pattern classification Optimum-Path Forest Supervised learning

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A Survey on Applications of Bipartite graph Edit Distance 11th

A Survey on Applications of Bipartite Graph Edit Distance

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11th IAPR-TC15 international workshop on graph-based representations in pattern recognition (GbRPR)

作者： Stauffer, Michael Tschachtli, thomas Fischer, Andreas Riesen, Kaspar Univ Appl Sci & Arts Northwestern Switzerland Inst Informat Syst Riggenbachstr 16 CH-4600 Olten Switzerland Univ Fribourg Dept Informat CH-1700 Fribourg Switzerland Univ Appl Sci & Arts Western Switzerland Inst Complex Syst CH-1705 Fribourg Switzerland Univ Pretoria Dept Informat Pretoria South Africa

ISBN: (纸本)9783319589619;9783319589602

About ten years ago, a novel graph edit distance framework based on bipartite graph matching has been introduced. this particular framework allows the approximation of graph edit distance in cubic time. this, in turn, makes the concept of graph edit distance also applicable to larger graphs. In the last decade the corresponding paper has been cited more than 360 times. Besides various extensions from the methodological point of view, we also observe a great variety of applications that make use of the bipartite graph matching framework. the present paper aims at giving a first survey on these applications stemming from six different categories (which range from document analysis, over biometrics to malware detection).

关键词： Applications of bipartite graph matching graph-based pattern representations

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Including geometry in graph representations: A quadratic-time graph isomorphism algorithm and its applications 6th

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6th international workshop on Structural and Syntactical pattern recognition, SSPR 1996

作者： Jiang, X.Y. Bunke, H. Dept. of Computer Science University of Bern Neubrückstrasse 10 BernCH-3012 Switzerland

ISBN: (纸本)3540615776

In graph representations of objects, geometric information is typically lost. this has forced researchers to use graph matching techniques that are intended to handle general graphs. By encoding the lost geometric information into graph representations, however, we can apply more efficient algorithms for constrained graphs. In this paper we introduce an edge ordering property that is satisfied in many applications. Given this property, the graph isomorphism problem is solvable in quadratic time. We discuss three concrete applications that can be reduced to the graph isomorphism problem and can thus profit from the quadratic-time graph isomorphism algorithm. the improved performance is demonstrated by simulation experiments. © Springer-Verlag Berlin Heidelberg 1996.

关键词： Geometry

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A Recursive Embedding Approach to Median graph Computation

A Recursive Embedding Approach to Median Graph Computation

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7th international workshop on graph-based representations in pattern recognition

作者： Ferrer, M. Karatzas, D. Valveny, E. Bunke, H. CSIC UPC Inst Robot & Informat Ind C Llorens Artigas 4-6 E-08028 Barcelona Spain Univ Autonoma Barcelona Ctr Vis Comp E-08193 Bellaterra Spain Univ Bern Inst Comp Sci & Appl Math CH-3012 Bern Switzerland

ISBN: (纸本)9783642021237

the median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper We present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments oil three databases show that this new approach is able to obtain better medians than the previous existing approaches.

关键词： graphic methods

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graph edit distance as a quadratic assignment problem

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pattern recognition LETTERS 2017年 87卷 38-46页

作者： Bougleux, Sebastien Brun, Luc Carletti, Vincenzo Foggia, Pasquale Gauzere, Benoit Vento, Mario Normandie Univ UNICAEN ENSICAEN CNRSGREYC UMR 6072 6 Bd Marechal Juin F-14050 Caen France Univ Salerno Dept Informat Elect Engn & Appl Math MIVIA Via Giovanni Paolo 2 I-84084 Fisciano SA Italy Normandie Univ INSA Rouen LITIS EA 4108 Ave Univ F-76801 St Etienne Du Rouvray France

the graph Edit Distance (GED) is a flexible measure of dissimilarity between graphs which arises in error-correcting graph matching. It is defined from an optimal sequence of edit operations (edit path) transforming one graph into another. Unfortunately, the exact computation of this measure is NP-hard. In the last decade, several approaches were proposed to approximate the GED in polynomial time, mainly by solving linear programming problems. Among them, the bipartite GED received much attention. It is deduced from a linear sum assignment of the nodes of the two graphs, which can be efficiently computed by Hungarian-type algorithms. However, edit operations on nodes and edges are not handled simultaneously, which limits the accuracy of the approximation. To overcome this limitation, we propose to extend the linear assignment model to a quadratic one. this is achieved through the definition of a family of edit paths induced by assignments between nodes. We formally show that the GED, restricted to the paths in this family, is equivalent to a quadratic assignment problem. Since this problem is NP-hard, we propose to compute an approximate solution by adapting two algorithms: Integer Projected Fixed Point method and Graduated Non Convexity and Concavity Procedure. Experiments show that the proposed approach is generally able to reach a more accurate approximation of the exact GED than the bipartite GED, with a computational cost that is still affordable for graphs of non trivial sizes. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Structural pattern recognition graph edit distance Edit paths Quadratic assignment problem Combinatorial optimization Relaxation methods

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Characteristic Polynomial Analysis on Matrix representations of graphs

Characteristic Polynomial Analysis on Matrix Representations...

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7th international workshop on graph-based representations in pattern recognition

作者： Ren, Peng Wilson, Richard C. Hancock, Edwin R. Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

ISBN: (纸本)9783642021237

Matrix representations for graphs play an important role in combinatorics. In this paper, we investigate four matrix representations for graphs and carry out an characteristic polynomial analysis upon them. the first two graph matrices are the adjacency matrix and Laplacian matrix. these two matrices call be obtained straightforwardly from graphs. the second two matrix representations, which are newly introduced [9][3], arc closely related with the Ihara zeta function and the discrete time quantum walk. they have a similar form and are established from a transformed graph. i.e. the oriented line graph of the original graph. We make use of the characteristic polynomial coefficients of the four matrices to characterize graphs and construct pattern spaces with a fixed dimensionality. Experimental results indicate that the two matrices in the transformed domain perform better than the two matrices in the original graph domain whereas the matrix associated with the Ihara zeta function is more efficient and effective than the matrix associated with the discrete time quantum walk and the remaining methods.

关键词： Matrix algebra

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