Topological data analysis

수학노트
둘러보기로 가기 검색하러 가기

노트

말뭉치

  1. Topological data analysis (TDA) is an emerging concept of data analysis for characterizing shape of data.[1]
  2. Topological data analysis (TDA) is a field of mathematics which deals with qualitative geometric features to analyze datasets.[2]
  3. On this page I have a number of items to get the interested reader started with persistent homology and topological data analysis (TDA).[3]
  4. 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.[4]
  5. In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology.[5]
  6. 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.[6]
  7. Statistical topological data analysis using persistence landscapes.[7]
  8. 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.[8]
  9. 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.[8]
  10. Our primary goal is to demonstrate the utility of topological data analysis for biological aggregations and similar applications.[8]
  11. We combine topological data analysis and machine learning to provide a collection of summary statistics describing patterns on both microscopic and macroscopic scales.[9]
  12. 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.[9]
  13. 27, topological data analysis (TDA) has emerged as a valuable tool for characterizing collective behavior and self-organization.[9]
  14. The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA).[10]
  15. But around this same time I kept hearing about an exciting but possibly over-hyped topic called topological data analysis: TDA.[11]
  16. One of the key messages around topological data analysis is that data has shape and the shape matters.[12]
  17. What is topological data analysis (TDA), and why is TDA taking the big data world by storm?[13]
  18. 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).[14]
  19. 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.[15]
  20. 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.[16]
  21. The development of this software will enable researchers at the Turing and elsewhere to apply topological data analysis at a previously unachievable scale.[16]
  22. 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.[16]
  23. 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).[17]
  24. Topological data analysis (TDA) aims to measure the “intrinsic shape” of data and identify this manifold despite noise and the likely nonlinear embedding.[18]
  25. Topological data analysis reveals the structure of data.[19]

소스

메타데이터

위키데이터

Spacy 패턴 목록

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