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Filipino sign language alphabet recognition using Persistent Homology Classification algorithm.

PeerJ. Computer science·2025
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A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments
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Persistent homology classification algorithm.

Mark Lexter D De Lara1,2

  • 1Institute of Mathematical Sciences and Physics, College of Arts and Sciences, University of the Philippines Los Baños, College, Los Baños, Laguna, Philippines.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary

This study introduces a novel data classification algorithm using persistent homology, a method analyzing data shape. The new algorithm performs comparably to or better than existing classifiers on various datasets.

Keywords:
Classification algorithmPersistent homologySupervised learningTopological data analysis

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Area of Science:

  • Machine Learning
  • Topological Data Analysis
  • Algebraic Topology

Background:

  • Data classification is crucial in machine learning, but no single classifier excels across all data types (No Free Lunch theorem).
  • Topological Data Analysis (TDA) is an emerging field focused on understanding data shape.
  • Persistent homology is a key TDA tool for quantifying topological features across resolutions.

Purpose of the Study:

  • To propose a supervised learning classification algorithm leveraging persistent homology.
  • To utilize persistence diagrams from training data classes for predicting new observations.

Main Methods:

  • Developed a supervised learning algorithm based on persistent homology.
  • Employed persistence diagrams to represent topological features of data classes.
  • Validated the algorithm on both real-world and synthetic datasets.
  • Compared performance against widely used classification algorithms.

Main Results:

  • The proposed persistent homology classification algorithm demonstrated competitive performance.
  • The algorithm performed on par with or superior to most compared classifiers.
  • Validation across diverse datasets confirmed the algorithm's efficacy.

Conclusions:

  • Persistent homology offers a powerful approach for data classification.
  • The developed algorithm provides a viable and effective alternative to existing methods.
  • This work highlights the potential of TDA in practical machine learning applications.