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Updated: Jun 23, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Graph classification by means of Lipschitz embedding.

Kaspar Riesen1, Horst Bunke

  • 1Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland. riesen@iam.unibe.ch

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|May 19, 2009
PubMed
Summary
This summary is machine-generated.

Graph representations offer an alternative to feature vectors in machine learning. New Lipschitz embeddings improve classification accuracy compared to original graph distances.

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Last Updated: Jun 23, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

  • Pattern Recognition
  • Machine Learning
  • Graph Theory

Background:

  • Graph-based representations are emerging as a versatile alternative to traditional feature vectors in pattern recognition.
  • Novel machine learning techniques like graph kernels and embeddings address the historical lack of graph classification algorithms.

Purpose of the Study:

  • To extend previous work on representing graphs via dissimilarities.
  • To explore the application of Lipschitz embeddings for graph classification.

Main Methods:

  • Utilizing Lipschitz embeddings to transform graph representations.
  • Comparing classification performance using original graph distances versus Lipschitz embedded graphs.

Main Results:

  • Classifiers employing Lipschitz embedded graphs demonstrated superior performance.
  • Empirical evaluation confirmed the effectiveness of the proposed embedding approach.

Conclusions:

  • Lipschitz embeddings provide an effective method for enhancing graph-based classification.
  • This approach overcomes limitations associated with direct use of graph distances in classification tasks.