Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernels and Distances for Structured Data
Machine Learning
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-theoretic techniques for web content mining
Graph-theoretic techniques for web content mining
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
Reducing the dimensionality of dissimilarity space embedding graph kernels
Engineering Applications of Artificial Intelligence
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
Classifier ensembles for vector space embedding of graphs
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Graph embedding in vector spaces by means of prototype selection
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
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
Theoretical and algorithmic framework for hypergraph matching
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Manifold Learning for Multi-classifier Systems via Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Study on Representations for Face Recognition from Thermal Images
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Graph embedding using constant shift embedding
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
From points to nodes: inverse graph embedding through a lagrangian formulation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Graph clustering using the Jensen-Shannon Kernel
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
Selecting structural base classifiers for graph-based multiple classifier systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Hypergraph-based image retrieval for graph-based representation
Pattern Recognition
Selecting feature lines in generalized dissimilarity representations for pattern recognition
Digital Signal Processing
On the informativeness of asymmetric dissimilarities
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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Recently, an emerging trend of representing objects by graphs can be observed. In fact, graphs offer a powerful alternative to feature vectors in pattern recognition, machine learning, and related fields. However, the domain of graphs contains very little mathematical structure, and consequently, there is only a limited amount of classification algorithms available. In this paper we survey recent work on graph embedding using dissimilarity representations. Once a population of graphs has been mapped to a vector space by means of this embedding procedure, all classification methods developed in statistical pattern recognition become directly available. In an experimental evaluation we show that the proposedmethodology of first embedding graphs in vector spaces and then applying a statistical classifier has significant potential to outperform classifiers that directly operate in the graph domain. Additionally, the proposed framework can be considered a contribution towards unifying the domains of structural and statistical pattern recognition.