A RKHS interpolator-based graph matching algorithm

Wyk, M.A. van; Durrani, T.S.; Wyk, Barend Jacobus van

doi:10.1109/tpami.2002.1017624

Cited by 54 publications

(29 citation statements)

References 42 publications

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“…The performance curves of the IBKPGM are almost identical to those of the RIGM [21] algorithm. This phenomenon cannot be explained at present, and is a topic for further investigation.…”

Section: Resultsmentioning

confidence: 73%

“…Up to this point the development coincides with that of the RIGM algorithm [21]. The way in which the permutation sub-matrix is inferred is totally different and is presented next.…”

Section: The Interpolator Equationmentioning

confidence: 68%

“…The performance of the IBKPGM algorithm was compared to the performance of the GAGM [1], EGM [10], CGGM [24], PTGM [9] and RIGM [21] algorithms. A comparison is also made against the Faugeras-Price Relaxation Labelling (FPRL) algorithm [25].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The RIGM algorithm [21] is based on the same theory, but the way in which the permutation sub-matrix P is obtained, is completely different.…”

Section: The Interpolator-based Approachmentioning

confidence: 99%

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Non-bayesian Graph Matching without Explicit Compatibility Calculations

Wyk

2002

Lecture Notes in Computer Science

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Abstract. This paper introduces a novel algorithm for performing Attributed Graph Matching (AGM). A salient characteristic of the Interpolator-Based Kronecker Product Graph Matching (IBKPGM) algorithm is that it does not require the explicit calculation of compatibility values between vertices and edges, either using compatibility functions or probability distributions. No assumption is made about the adjacency structure of the graphs to be matched. The IBKPGM algorithm uses Reproducing Kernel Hilbert Space (RKHS) interpolator theory to obtain an unconstrained estimate to the Kronecker Match Matrix (KMM) from which a permutation sub-matrix is inferred.

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“…The performance curves of the IBKPGM are almost identical to those of the RIGM [21] algorithm. This phenomenon cannot be explained at present, and is a topic for further investigation.…”

Section: Resultsmentioning

confidence: 73%

“…Up to this point the development coincides with that of the RIGM algorithm [21]. The way in which the permutation sub-matrix is inferred is totally different and is presented next.…”

Section: The Interpolator Equationmentioning

confidence: 68%

Section: Numerical Resultsmentioning

confidence: 99%

“…The RIGM algorithm [21] is based on the same theory, but the way in which the permutation sub-matrix P is obtained, is completely different.…”

Section: The Interpolator-based Approachmentioning

confidence: 99%

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Non-bayesian Graph Matching without Explicit Compatibility Calculations

Wyk

2002

Lecture Notes in Computer Science

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“…Almohamad and Duffuaa [1] suggested a linear programming approach to the weighted graph matching problem. Van Wyk et al [26] presented a graph matching algorithm from the functional interpolation theory point of view.…”

Section: Graph Matchingmentioning

confidence: 99%

Finding email correspondents in online social networks

et al. 2012

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Email correspondents play an important role in many people's social networks. Finding email correspondents in social networks accurately, though may seem to be straightforward at a first glance, is challenging. Most of the existing online social networking sites recommend possible matches by comparing the information of email accounts and social network profiles, such as display names and email addresses. However, as shown empirically in this paper, such methods may not be effective in practice. To the best of our knowledge, this problem has not been carefully and thoroughly addressed in research.In this paper, we systematically investigate the problem and develop a practical data mining approach. We find that using only the profiles or the graph structures is far from effective. Our method utilizes the similarity between email accounts and social network user profiles, and at the same time explores the similarity between the email communication network and the social network under investigation. We demonstrate the effectiveness of our method using two real data sets on emails and Facebook. * We are grateful to the anonymous reviewers for their constructive suggestions, which help to improve the quality of this paper.

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Graph Matching – Challenges and Potential Solutions

Bunke

Irniger

Neuhaus

2005

Image Analysis and Processing – ICIAP 2005

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Abstract. Structural pattern representations, especially graphs, have advantages over feature vectors. However, they also suffer from a number of disadvantages, for example, their high computational complexity. Moreover, we observe that in the field of statistical pattern recognition a number of powerful concepts emerged recently that have no equivalent counterpart in the domain of structural pattern recognition yet. Examples include multiple classifier systems and kernel methods. In this paper, we survey a number of recent developments that may be suitable to overcome some of the current limitations of graph based representations in pattern recognition.

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A RKHS interpolator-based graph matching algorithm

Cited by 54 publications

References 42 publications

Non-bayesian Graph Matching without Explicit Compatibility Calculations

Non-bayesian Graph Matching without Explicit Compatibility Calculations

Finding email correspondents in online social networks

Graph Matching – Challenges and Potential Solutions

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