LEADER 04596nam 22004695 450 001 9910338251903321 005 20200703070039.0 010 $a3-030-11566-6 024 7 $a10.1007/978-3-030-11566-1 035 $a(CKB)4100000007817055 035 $a(MiAaPQ)EBC5741616 035 $a(DE-He213)978-3-030-11566-1 035 $a(PPN)242824684 035 $a(EXLCZ)994100000007817055 100 $a20191027d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aResearch in Data Science /$fedited by Ellen Gasparovic, Carlotta Domeniconi 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (302 pages) 225 1 $aAssociation for Women in Mathematics Series,$x2364-5733 ;$v17 311 $a3-030-11565-8 327 $aPreface -- N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin: Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors -- P. Mani, M. Vazquez, J. R. Metcalf-Burton, C. Domeniconi, H. Fairbanks, G. Bal, E. Beer, and S. Tari: The Hubness Phenomenon in High Dimensional Spaces -- F. P. Medina, L. Ness, M. Weber, and K. Y. Djima: Heuristic Framework for Multiscale Testing of the Multi-Manifold Hypothesis -- K. Leonard, Y. Zhou, X. Wang, and G. Heo: High-dimensional Multiple Scaled Data Analysis of Obstructive Sleep Apnea Study with Interdisciplinary Endeavor -- E. Munch and A. Stefanou: The L(infinity)-Cophenetic Metric for Phylogenetic Trees as an Interleaving Distance -- L. Ness: Inference of a Dyadic Measure and its Simplicia Geometry from Binary Feature Data and Application to Data Quality -- A. Genctav, M. Genctav, and S. Tari: A Non-local Measure for Mesh Saliency via Feature Space Reduction -- F. Seeger, A. Little, Y. Chen, T. Woolf, H. Cheng, and J. C. Mitchell: Feature Design for Protein Interface Hotspots using KFC2 and Rosetta -- R. Aroutiounian, K. Leonard, R. Moreno, R. Teufel: Geometry-Based Classification for Automated Schizophrenia Diagnosis -- N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin: Compressed Anomaly Detection with Multiple Mixed Observations -- A. Grim, B. Iskra, N. Ju, A. Kryshchenko, F. P. Medina, L. Ness, M. Ngamini, M. Owen, R. Paffenroth, and S. Tang: Analysis of Simulated Crowd Flow Exit Data: Visualization, Panic Detection, and Exit Time Convergence, Attribution and Estimation -- V. Adanova and S. Tari: A Data Driven Modeling of Ornaments. . 330 $aThis edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas. Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data. Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community. The volume is suitable for researchers in data science in industry and academia. . 410 0$aAssociation for Women in Mathematics Series,$x2364-5733 ;$v17 606 $aComputer science?Mathematics 606 $aComputer mathematics 606 $aMathematical Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M13110 615 0$aComputer science?Mathematics. 615 0$aComputer mathematics. 615 14$aMathematical Applications in Computer Science. 676 $a502.85 702 $aGasparovic$b Ellen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDomeniconi$b Carlotta$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910338251903321 996 $aResearch in Data Science$91733520 997 $aUNINA