Research in Data Science
Editors: Gasparovic, Ellen, Domeniconi, Carlotta (Eds.)
Free Preview- Highlights the fundamental role of mathematics in the development of data scienceFeatures a diverse range of topics from the theoretical to the applied and computationalBased on the 2017 Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop
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- About this book
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This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas. Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data. Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community. The volume is suitable for researchers in data science in industry and academia.
- About the authors
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- Table of contents (12 chapters)
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Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors
Pages 1-14
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The Hubness Phenomenon in High-Dimensional Spaces
Pages 15-45
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Heuristic Framework for Multiscale Testing of the Multi-Manifold Hypothesis
Pages 47-80
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Interdisciplinary Approaches to Automated Obstructive Sleep Apnea Diagnosis Through High-Dimensional Multiple Scaled Data Analysis
Pages 81-107
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The ℓ ∞-Cophenetic Metric for Phylogenetic Trees As an Interleaving Distance
Pages 109-127
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Table of contents (12 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Research in Data Science
- Editors
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- Ellen Gasparovic
- Carlotta Domeniconi
- Series Title
- Association for Women in Mathematics Series
- Series Volume
- 17
- Copyright
- 2019
- Publisher
- Springer International Publishing
- Copyright Holder
- The Author(s) and the Association for Women in Mathematics
- eBook ISBN
- 978-3-030-11566-1
- DOI
- 10.1007/978-3-030-11566-1
- Hardcover ISBN
- 978-3-030-11565-4
- Series ISSN
- 2364-5733
- Edition Number
- 1
- Number of Pages
- XIV, 297
- Number of Illustrations
- 14 b/w illustrations, 106 illustrations in colour
- Topics
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