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• W4285194969 abstract "An increasing demand exists for innovative data analysis methods in a world where various data are generated by complicated data collecting processes. Several hierarchical data sets with high-dimensional data are the focus of this research. If a data set has complex samples, we assume that every sample has an irregular structure that can be represented in the form of an unordered graph. Topological data analysis (TDA) and geometric manifold learning are combined in this study, which add a significant amount of originality. However, topology gives both global and local descriptors, whereas topology is a different approach. We provide a method for combining these two features to create an understandable visual representation of hierarchical data sets. An entirely new metric for quantitative structural analysis at the sample level was devised with the aid of manifold learning. When working with huge data sets, TDA is often utilised to extract structural and qualitative information. A set of hyper-spectral photos and simulated data show how well this approach works and how widely applicable it is. Here, we show that the hierarchical structure of hyper-spectral images is optimum. Our new method also produces better classification results than current best practices, as demonstrated by our experiments." @default.
• W4285194969 created "2022-07-14" @default.
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• W4285194969 date "2022-01-01" @default.
• W4285194969 modified "2023-10-03" @default.
• W4285194969 title "Topological Machine Learning Data Analysis for the Extraction of Robust Geometric Information" @default.
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• W4285194969 doi "https://doi.org/10.1007/978-981-19-1559-8_17" @default.
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