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* [{'LOWER': 'topological'}, {'LOWER': 'data'}, {'LEMMA': 'analysis'}]

2021년 10월 19일 (화) 00:42 판

노트

  • Topological data analysis (TDA) is an emerging concept of data analysis for characterizing shape of data.[1]
  • Topological data analysis (TDA) is a field of mathematics which deals with qualitative geometric features to analyze datasets.[2]
  • We demonstrate the utility of topological data analysis combined with MC and present its merits and disadvantages.[3]
  • The emerging area of topological data analysis (TDA) is a promising avenue of research to answer the challenge.[4]
  • Chung is a leading expert of Topological Data analysis and has published more than 20 peer reviewed papers on this topic.[4]
  • Wang is a leading expert on Topological Data Analysis in signal processing, particularly in electroencephalographic signals.[4]
  • The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA).[5]
  • In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology.[6]
  • One way to apply statistics to topological data analysis is to study the statistical properties of topological features of point clouds.[6]
  • Topological data analysis and persistent homology have had impacts on Morse theory.[6]
  • Towards a new approach to reveal dynamical organization of the brain using topological data analysis.[7]
  • But around this same time I kept hearing about an exciting but possibly over-hyped topic called topological data analysis: TDA.[8]
  • In this article, I’ll specifically break down what Topological Data Analysis is and how to think about it.[9]
  • How is Topology related to Topological Data Analysis?[9]
  • These aspects are all essential in the field of Persistent Homology, which is the main tool that Topological Data Analysis is inspired by.[9]
  • Topological data analysis reveals the structure of data.[10]
  • This is a very important work that shows the way to applications of topological data analysis in genomics.[10]
  • 27, topological data analysis (TDA) has emerged as a valuable tool for characterizing collective behavior and self-organization.[11]

소스

  1. ↑ Topological Data Analysis Team
  2. ↑ What Is Topological Data Analysis? Data Defined
  3. ↑ Topological data analysis in digital marketing
  4. ↑ 4.0 4.1 4.2 Online Learning Series
  5. ↑ Topological Data Analysis and Beyond
  6. ↑ 6.0 6.1 6.2 Topological data analysis
  7. ↑ Topological Data Analysis of Vascular Disease: A Theoretical Framework
  8. ↑ A Mathematician's Perspective on Topological Data Analysis and R
  9. ↑ 9.0 9.1 9.2 Topological Data Analysis — Unpacking the Buzzword
  10. ↑ 10.0 10.1 Topological Data Analysis for Genomics and Evolution
  11. ↑ Topological data analysis of zebrafish patterns

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노트

말뭉치

  1. On this page I have a number of items to get the interested reader started with persistent homology and topological data analysis (TDA).[1]
  2. In topological data analysis, one usually replaces the original space with one or more topological spaces that one hopes will retain the relevant topological information in the original set.[2]
  3. In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology.[3]
  4. Topological data analysis (tda) is a recent and fast-growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data.[4]
  5. Statistical topological data analysis using persistence landscapes.[5]
  6. Abstract We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms.[6]
  7. In brief, we use the methods of topological data analysis to compute the persistent homology of spatiotemporal aggregation data sets arising from numerical simulation of models.[6]
  8. Our primary goal is to demonstrate the utility of topological data analysis for biological aggregations and similar applications.[6]
  9. We combine topological data analysis and machine learning to provide a collection of summary statistics describing patterns on both microscopic and macroscopic scales.[7]
  10. Here we introduce methods based on topological data analysis and interpretable machine learning for quantifying both agent-level features and global pattern attributes on a large scale.[7]
  11. 27, topological data analysis (TDA) has emerged as a valuable tool for characterizing collective behavior and self-organization.[7]
  12. The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA).[8]
  13. But around this same time I kept hearing about an exciting but possibly over-hyped topic called topological data analysis: TDA.[9]
  14. One of the key messages around topological data analysis is that data has shape and the shape matters.[10]
  15. What is topological data analysis (TDA), and why is TDA taking the big data world by storm?[11]
  16. Introduction Topological data analysis (TDA) consists of a growing set of methods that provide insight to the shape of data (see the surveys Ghrist, 2008; Carlsson, 2009).[12]
  17. Topological data analysis (TDA) involves extracting information from clouds of data points and using the information to classify data, recognize patterns or predict trends, for example.[13]
  18. Introduction Topological data analysis (TDA) describes the shape of noisy and potentially incomplete data in a robust way, so that such data can be better understood and utilised.[14]
  19. The development of this software will enable researchers at the Turing and elsewhere to apply topological data analysis at a previously unachievable scale.[14]
  20. It will also lead to the development of new techniques that make topological data analysis more robust to the presence of egregious outliers in large datasets.[14]
  21. In this paper, we present an alternative approach to AR pattern recognition based on topological data analysis (TDA) (Ghrist, 2008; Carlsson, 2009, 2014) and machine learning (ML) (Kubat, 2015).[15]
  22. Topological data analysis (TDA) aims to measure the “intrinsic shape” of data and identify this manifold despite noise and the likely nonlinear embedding.[16]
  23. Topological data analysis reveals the structure of data.[17]

소스

  1. ↑ Topological Data Analysis
  2. ↑ Topological Data Analysis • IMSI
  3. ↑ Topological data analysis
  4. ↑ An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists
  5. ↑ A User’s Guide to Topological Data Analysis
  6. ↑ 6.0 6.1 6.2 Topological Data Analysis of Biological Aggregation Models
  7. ↑ 7.0 7.1 7.2 Topological data analysis of zebrafish patterns
  8. ↑ Topological Data Analysis and Beyond
  9. ↑ A Mathematician's Perspective on Topological Data Analysis and R
  10. ↑ What Is Topological Data Analysis? Data Defined
  11. ↑ Introduction to Topological Data Analysis
  12. ↑ Journal of machine learning research 16 (2015) 77-102
  13. ↑ Topological data analysis can help predict crashes
  14. ↑ 14.0 14.1 14.2 Scalable topological data analysis
  15. ↑ Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
  16. ↑ Topological data analysis: What is persistent homology?
  17. ↑ Topological Data Analysis for Genomics and Evolution

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