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...
详细信息
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 traveling salesperson problem (TSP) is difficult to solve for input instances with large number of cities. Instead of finding the solution of an input with a large number of cities, the problem is approximated int...
详细信息
ISBN:
(纸本)9783540729020
the traveling salesperson problem (TSP) is difficult to solve for input instances with large number of cities. Instead of finding the solution of an input with a large number of cities, the problem is approximated into a simpler form containing smaller number of cities, which is then solved optimally. graph pyramid solution strategies, in a bottom-up manner using Boruvka's minimum spanning tree, convert a 2D Euclidean TSP problem with a large number of cities into successively smaller problems (graphs) with similar layout and solution, until the number of cities is small enough to seek the optimal solution. Expanding this tour solution in a top-down manner to the lower levels of the pyramid approximates the solution. the new model has an adaptive spatial structure and it simulates visual acuity and visual attention. the model solves the TSP problem sequentially, by moving attention from city to city withthe same quality as humans. graph pyramid data structures and processing strategies are a plausible model for finding near-optimal solutions for computationally hard patternrecognition problems.
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...
详细信息
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.
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...
详细信息
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...
详细信息
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...
详细信息
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.
the paper presents a novel hierarchical approach to test pattern generation for sequential circuits based on an input model of mixed-level decision diagrams. A method that handles, both, data and control parts of the ...
详细信息
the paper presents a novel hierarchical approach to test pattern generation for sequential circuits based on an input model of mixed-level decision diagrams. A method that handles, both, data and control parts of the design in a uniform manner is proposed. the method combines deterministic and simulation-based techniques. On the register-transfer level, deterministic path activation is combined with simulation based-techniques used for constraints solving. the gate-level local test patterns for components are randomly generated driven by high-level constraints and partial path activation solutions. Experiments show that high fault coverages for circuits with complex sequential structures can be achieved in a very short time by using this approach.
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,...
详细信息
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 this paper, we present a novel approach that assists in the task of data-parallel patternrecognition. the classification of program code into parallel patterns relies mainly in the extraction of characteristics th...
详细信息
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...
详细信息
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.
暂无评论