Transforming strings to vector spaces using prototype selection

Authors:
Barbara Spillmann;Michel Neuhaus;Horst Bunke;Elżbieta Pękalska;Robert P. W. Duin
Affiliations:
Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland;Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, CD, The Netherlands;Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, CD, The Netherlands
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

Citing 11
Cited 23

Algorithms for clustering data

Algorithms for clustering data
Extended attributed string matching for shape recognition

Computer Vision and Image Understanding
The String-to-String Correction Problem

Journal of the ACM (JACM)
Comparison of Four Initialization Techniques for the K -Medians Clustering Algorithm

Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Properties of Embedding Methods for Similarity Searching in Metric Spaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate Stroke Sequence String Matching Algorithm for Character Recognition and Analysis

ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Pattern Vectors from Algebraic Graph Theory

IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype selection for dissimilarity-based classifiers

Pattern Recognition
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)

Graph Classification Based on Dissimilarity Space Embedding

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Use of Structured Pattern Representations for Combining Classifiers

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Reducing the dimensionality of dissimilarity space embedding graph kernels

Engineering Applications of Artificial Intelligence
An input panel and recognition engine for on-line handwritten text recognition

Proceedings of the 2007 conference on Artificial Intelligence Research and Development
When Semi-supervised Learning Meets Ensemble Learning

MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes

MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Prototype selection based on sequential search

Intelligent Data Analysis
Graph classification by means of Lipschitz embedding

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Mixed data object selection based on clustering and border objects

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A review of instance selection methods

Artificial Intelligence Review
A similarity-based approach for shape classification using Aslan skeletons

Pattern Recognition Letters
Recent advances in graph-based pattern recognition with applications in document analysis

Pattern Recognition
Graph embedding using constant shift embedding

ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Improving vector space embedding of graphs through feature selection algorithms

Pattern Recognition
Towards the unification of structural and statistical pattern recognition

Pattern Recognition Letters
The dissimilarity space: Bridging structural and statistical pattern recognition

Pattern Recognition Letters
Generalized median string computation by means of string embedding in vector spaces

Pattern Recognition Letters
The dissimilarity representation for structural pattern recognition

CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Discriminative prototype selection methods for graph embedding

Pattern Recognition
Flexible and efficient string similarity search with alignment-space transform

Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
A new proposal for graph classification using frequent geometric subgraphs

Data & Knowledge Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

A common way of expressing string similarity in structural pattern recognition is the edit distance. It allows one to apply the kNN rule in order to classify a set of strings. However, compared to the wide range of elaborated classifiers known from statistical pattern recognition, this is only a very basic method. In the present paper we propose a method for transforming strings into n-dimensional real vector spaces based on prototype selection. This allows us to subsequently classify the transformed strings with more sophisticated classifiers, such as support vector machine and other kernel based methods. In a number of experiments, we show that the recognition rate can be significantly improved by means of this procedure.