Learning Topology: Bridging Computational Topology and Machine Learning

Moroni, Davide; Pascali, Maria Antonietta

doi:10.1134/s1054661821030184

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…Applications of persistent homology span from computer vision and shape analysis to biomedical imaging and complex networks analysis. In this perspective, one of the most promising trends is the merging of persistent homology with machine and deep learning [6].…”

Section: A Computational Topologymentioning

confidence: 99%

Analysis of sea surface temperature maps via topological machine learning

Conti

Papini

Moroni

et al. 2023

2023 IX International Conference on Information Technology and Nanotechnology (ITNT)

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Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.

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Section: A Computational Topologymentioning

confidence: 99%

Analysis of sea surface temperature maps via topological machine learning

Conti

Papini

Moroni

et al. 2023

2023 IX International Conference on Information Technology and Nanotechnology (ITNT)

Self Cite

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“…A long lifespan is considered a prominent feature represented by points far from the diagonal in the diagram. In contrast, short lifespans, represented by points close to the diagonal, are interpreted as noise [22].…”

Section: Introductionmentioning

confidence: 99%

Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection

Malek

Alias

Razak

et al. 2023

Cancers

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Microcalcifications in mammogram images are primary indicators for detecting the early stages of breast cancer. However, dense tissues and noise in the images make it challenging to classify the microcalcifications. Currently, preprocessing procedures such as noise removal techniques are applied directly on the images, which may produce a blurry effect and loss of image details. Further, most of the features used in classification models focus on local information of the images and are often burdened with details, resulting in data complexity. This research proposed a filtering and feature extraction technique using persistent homology (PH), a powerful mathematical tool used to study the structure of complex datasets and patterns. The filtering process is not performed directly on the image matrix but through the diagrams arising from PH. These diagrams will enable us to distinguish prominent characteristics of the image from noise. The filtered diagrams are then vectorised using PH features. Supervised machine learning models are trained on the MIAS and DDSM datasets to evaluate the extracted features’ efficacy in discriminating between benign and malignant classes and to obtain the optimal filtering level. This study reveals that appropriate PH filtering levels and features can improve classification accuracy in early cancer detection.

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“…These properties allow TDA to take advantage of the topological information to process the data further and perform various machine learning tasks, e.g., classification, clustering, etc. (Moroni and Pascali, 2021).…”

Section: Introductionmentioning

confidence: 99%

Prediction of cybersickness in virtual environments using topological data analysis and machine learning

Hadadi

Guillet

Chardonnet

et al. 2022

Front. Virtual Real.

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Recent significant progress in Virtual Reality (VR) applications and environments raised several challenges. They proved to have side effects on specific users, thus reducing the usability of the VR technology in some critical domains, such as flight and car simulators. One of the common side effects is cybersickness. Some significant commonly reported symptoms are nausea, oculomotor discomfort, and disorientation. To mitigate these symptoms and consequently improve the usability of VR systems, it is necessary to predict the incidence of cybersickness. This paper proposes a machine learning approach to VR’s cybersickness prediction based on physiological and subjective data. We investigated combinations of topological data analysis with a range of classifier algorithms and assessed classification performance. The highest performance of Topological Data Analysis (TDA) based methods was achieved in combination with SVMs with Gaussian RBF kernel, indicating that Gaussian RBF kernels provide embeddings of physiological time series data into spaces that are rich enough to capture the essential geometric features of this type of data. Comparing several combinations with feature descriptors for physiological time series, the performance of the TDA + SVM combination is in the top group, statistically being on par or outperforming more complex and less interpretable methods. Our results show that heart rate does not seem to correlate with cybersickness.

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Learning Topology: Bridging Computational Topology and Machine Learning

Cited by 9 publications

References 53 publications

Analysis of sea surface temperature maps via topological machine learning

Analysis of sea surface temperature maps via topological machine learning

Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection

Prediction of cybersickness in virtual environments using topological data analysis and machine learning

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