Proceedings of International Conference on Applied Innovation in IT
2023/03/09, Volume 11, Issue 1, pp.61-66
Persistent Homology in Machine Learning: Applied Sciences Review
Oleksandr Yavorskyi, Andrii Asseko-Nkili and Nataliia Kussul Abstract: Topological Data Analysis (‘TDA’) has become a vibrant and quickly developing field in recent years, providing topology-enhanced data processing and Machine Learning (‘ML’) applications. Due to the novelty of the field, as well as the dissimilarity between the mathematics behind the classical ML and TDA, it might be complicated for a field newcomer to assess the feasibility of the approaches proposed by TDA and the relevancy of the possible applications. The current paper aims to provide an overview of the recent developments that relate to persistent homology, a part of the mathematical machinery behind the TDA, with a particular focus on applied sciences. We consider multiple areas, such as physics, healthcare, material sciences, and others, examining the recent developments in the field. The resulting summary of this paper could be used by field experts to expand their knowledge on recent persistent homology applications, while field newcomers could assess the applicability of this TDA approach for their research. We also point out some of the current restrictions on the use of persistent homology, as well as potential development trajectories that might be useful to the whole field.
Keywords: Algebraic Topology, Persistent Homology, Machine Learning, Physics, Healthcare, Topological Data Analysis, Chemistry, Biology, Material Sciences, Data Processing.
DOI: 10.25673/101914
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References:
- F. Hensel, M. Moor, and B. Rieck, “A Survey of Topological Machine Learning Methods,” Front. Artif. Intell., Sec. Machine Learning and Artificial Intelligence, May 2021.
- H. Edelsbrunner and J. Harer, “Computational Topology: An Introduction,” 2010.
- A. Klenke, “Probability Theory,” Berlin: Springer, 191 p., 2008.
- H. Adams et al., “Persistence Images: A Stable Vector Representation of Persistent Homology,” Journal of Machine Learning Research, vol. 18, pp. 1-35, 2017.
- P. Frosini and C. Landi, “Persistent Betti numbers for a noise tolerant shape-based approach to image retrieval,” Pattern Recognition Letters, vol. 34 pp. 863-872, 2013.
- G. Carlsson, T. Ishkhanov, V. Silva, and A. Zomorodian, “On the local behavior of spaces of natural images,” International Journal of Computer Vision, vol. 76, pp. 1-12, 2008.
- F. Takens, “Detecting strange attractors in turbulence,” Lecture Notes in Mathematics. pp. 366-381, 1981.
- A. Karan and A. Kaygun, “Time series classification via topological data analysis,” Expert Systems with Applications, vol. 183, November 2021.
- S. Majumdar and A.K. Laha, “Clustering and classification of time series using topological data analysis with applications to finance,” Expert Systems with Applications, vol. 162, December 2020.
- Z. Cang and G.W. Wei, “Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction,” International Journal for Numerical Methods in Biomedical Engineering, vol. 34, 2018.
- J. Townsend, C.P. Micucci, J.H. Hymel, V. Maroulas, and K.D. Vogiatzis “Representation of molecular structures with persistent homology for machine learning applications in chemistry,” Nat. Commun., vol. 11, 2020.
- X. J. Zhu, “Persistent homology: An introduction and a new text representation for natural language processing,” IJCAI, pp. 1953-1959, 2013.
- B.W. Xin, J. Huang, L. Zhang, and et al., “Dynamic topology analysis for spatial patterns of multifocal lesions on MRI,” Medical Image Analysis, vol. 76, 2022.
- A.T. Jafadideh and B.M. Asl, “Topological analysis of brain dynamics in autism based on graph and persistent homology,” Computers in Biology and Medicine, vol. 150, 2022.
- Y. Chiang, W.H. Hui, and S.W. Chang, “Encoding protein dynamic information in graph representation for functional residue identification,” Cell Reports Physical Science, vol. 3, July 2022.
- I. Morilla and Ph. Chan, “Deep models of integrated multiscale molecular data decipher the endothelial cell response to ionizing radiation,” iScience, vol. 25, January 2022.
- S.S. Sørensena, T. Du, C. Biscio, L. Fajstrup, and M.M. Smedskjaer, “Persistent homology: A tool to understand medium-range order glass structure,” Journal of Non-Crystalline Solids: X, vol. 16, December 2022.
- D.P. Gao, J.H. Chen, Z.T. Dong, and H.W. Lin, “Connectivity-guaranteed porous synthesis in free form model by persistent homology,” Computers & Graphics, vol. 106, pp. 33-44, 2022.
- T. Kojimaab, T. Washiob, S. Harab, and M. Koishia, “Search strategy for rare microstructure to optimize material properties of filled rubber using machine learning based simulation,” Computational Materials Science, vol. 204, March 2022.
- S. Casolo, “Severe slugging flow identification from topological indicators,” Digital Chemical Engineering, vol. 4, September 2022.
- M.C. Yesilli, F.A. Khasawneh, and B.P. Mann, “Transfer learning for autonomous chatter detection in machining,” Journal of Manufacturing Processes, vol. 80, pp. 1-27, August 2022.
- T. Jiang, M. Huang, I. Segovia-Dominguez, N. Newlands, and Y.R. Gel, “Learning Space-Time Crop Yield Patterns with Zigzag Persistence-Based LSTM: Toward More Reliable Digital Agriculture Insurance,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, 2022.
- J. Cisewski-Kehe, B.T. Fasy, W. Hellwing, M.R. Lovell, P. Drozda, and M. Wu, “Differentiating small-scale subhalo distributions in CDM and WDM models using persistent homology,” Phys. Rev. D, vol. 106, July 2022.
- D. Packwood et al., “Machine Learning in Materials Chemistry: An Invitation,” Machine Learning with Applications, vol. 8, June 2022.
- Ch. Chinmayee, N.A. Murugan, and U.D. Priyakumar, “Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods,” Drug Discovery Today, vol. 27, pp. 1847-1861, July 2022.
- Y. Skaf and R. Laubenbacher, “Topological data analysis in biomedicine: A review,” Journal of Biomedical Informatics, vol. 130, June 2022.
- C-E. Minciuna and et al., “The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers,” Computational and Structural Biotechnology Journal, vol. 20, pp. 5065-5075, 2022.
- S. Prabhu, K. Prasad, A. Robels-Kelly, and X. Lu, “AI-based carcinoma detection and classification using histopathological images: A systematic review,” Computers in Biology and Medicine, vol. 142, March 2022.
- C. Bodnar and et al., “Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks,” Proceedings of the 38th International Conference on Machine Learning, PMLR, vol. 139, pp. 1-12, 2021.
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