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 s...
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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 withthe left-out time-series data features and the node prior to completing the classification task of the brain network. In comparison withthe latest research methods, our approach achieves higher classification accuracy.
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 mu...
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.
Structural patternrecognition 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. Coh...
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ISBN:
(纸本)9783642021237
Structural patternrecognition 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 patternrecognition 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 patternrecognition are discussed.
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 f...
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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 graphtheory 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.
graph-basedpatternrecognition 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 ga...
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graph-basedpatternrecognition 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.
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,...
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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).
In graphrepresentations 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 inf...
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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 extreme...
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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.
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 o...
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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.
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 ...
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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 withthe 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 withthe Ihara zeta function is more efficient and effective than the matrix associated withthe discrete time quantum walk and the remaining methods.
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