An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Fast Nearest-Neighbor Search in Dissimilarity Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing approximate tree edit distance using relaxation labeling
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster-preserving Embedding of Proteins
Cluster-preserving Embedding of Proteins
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Levenshtein Distance for Graph Spectral Features
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing Hierarchical Structures Using Graph Spectra
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-Theoretic Techniques for Web Content Mining
Graph-Theoretic Techniques for Web Content Mining
A Binary Linear Programming Formulation of the Graph Edit Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
A Riemannian approach to graph embedding
Pattern Recognition
On Lipschitz Embeddings of Graphs
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Transforming strings to vector spaces using prototype selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Report on the second symbol recognition contest
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Maximum likelihood for gaussians on graphs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Generalized learning graph quantization
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Automatic human action recognition in videos by graph embedding
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Efficient retrieval of 3D building models using embeddings of attributed subgraphs
Proceedings of the 20th ACM international conference on Information and knowledge management
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
Maximum likelihood method for parameter estimation of bell-shaped functions on graphs
Pattern Recognition Letters
Human action recognition in video by fusion of structural and spatio-temporal features
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Graph classification with imbalanced class distributions and noise
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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In pattern recognition and related fields, graph-based representations offer a versatile alternative to the widely used feature vectors. Therefore, an emerging trend of representing objects by graphs can be observed. This trend is intensified by the development of novel approaches in graph-based machine learning, such as graph kernels or graph-embedding techniques. These procedures overcome a major drawback of graphs, which consists of a serious lack of algorithms for classification. This paper is inspired by the idea of representing graphs through dissimilarities and extends our previous work to the more general setting of Lipschitz embeddings. In an experimental evaluation, we empirically confirm that classifiers that rely on the original graph distances can be outperformed by a classification system using the Lipschitz embedded graphs.