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* [{'LOWER': 'topological'}, {'LOWER': 'data'}, {'LEMMA': 'analysis'}] | * [{'LOWER': 'topological'}, {'LOWER': 'data'}, {'LEMMA': 'analysis'}] | ||
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+ | == 노트 == | ||
+ | |||
+ | ===말뭉치=== | ||
+ | # On this page I have a number of items to get the interested reader started with persistent homology and topological data analysis (TDA).<ref name="ref_37b6a0f0">[https://people.clas.ufl.edu/peterbubenik/intro-to-tda/ Topological Data Analysis]</ref> | ||
+ | # 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.<ref name="ref_0acb0af5">[https://www.imsi.institute/activities/topological-data-analysis/ Topological Data Analysis • IMSI]</ref> | ||
+ | # In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology.<ref name="ref_f64311da">[https://en.wikipedia.org/wiki/Topological_data_analysis Topological data analysis]</ref> | ||
+ | # 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.<ref name="ref_6861ec47">[https://www.frontiersin.org/articles/10.3389/frai.2021.667963/full An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists]</ref> | ||
+ | # Statistical topological data analysis using persistence landscapes.<ref name="ref_bb5e6fa3">[https://learning-analytics.info/index.php/JLA/article/view/5196 A User’s Guide to Topological Data Analysis]</ref> | ||
+ | # 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.<ref name="ref_821e2c82">[https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0126383 Topological Data Analysis of Biological Aggregation Models]</ref> | ||
+ | # 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.<ref name="ref_821e2c82" /> | ||
+ | # Our primary goal is to demonstrate the utility of topological data analysis for biological aggregations and similar applications.<ref name="ref_821e2c82" /> | ||
+ | # We combine topological data analysis and machine learning to provide a collection of summary statistics describing patterns on both microscopic and macroscopic scales.<ref name="ref_dd596447">[https://www.pnas.org/content/117/10/5113 Topological data analysis of zebrafish patterns]</ref> | ||
+ | # 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.<ref name="ref_dd596447" /> | ||
+ | # 27, topological data analysis (TDA) has emerged as a valuable tool for characterizing collective behavior and self-organization.<ref name="ref_dd596447" /> | ||
+ | # The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA).<ref name="ref_5fe90793">[https://tda-in-ml.github.io/ Topological Data Analysis and Beyond]</ref> | ||
+ | # But around this same time I kept hearing about an exciting but possibly over-hyped topic called topological data analysis: TDA.<ref name="ref_a0f621f5">[https://rviews.rstudio.com/2018/11/14/a-mathematician-s-perspective-on-topological-data-analysis-and-r/ A Mathematician's Perspective on Topological Data Analysis and R]</ref> | ||
+ | # One of the key messages around topological data analysis is that data has shape and the shape matters.<ref name="ref_046650b0">[https://www.indicative.com/resource/topological-data-analysis/ What Is Topological Data Analysis? Data Defined]</ref> | ||
+ | # What is topological data analysis (TDA), and why is TDA taking the big data world by storm?<ref name="ref_427229b6">[https://www.ayasdi.com/social/TDAintroduction/ Introduction to Topological Data Analysis]</ref> | ||
+ | # 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).<ref name="ref_f57917da">[https://www.jmlr.org/papers/volume16/bubenik15a/bubenik15a.pdf Journal of machine learning research 16 (2015) 77-102]</ref> | ||
+ | # 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.<ref name="ref_c3a618e5">[https://actu.epfl.ch/news/topological-data-analysis-can-help-predict-crashes/ Topological data analysis can help predict crashes]</ref> | ||
+ | # 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.<ref name="ref_ee8f29ef">[https://www.turing.ac.uk/research/research-projects/scalable-topological-data-analysis Scalable topological data analysis]</ref> | ||
+ | # The development of this software will enable researchers at the Turing and elsewhere to apply topological data analysis at a previously unachievable scale.<ref name="ref_ee8f29ef" /> | ||
+ | # 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.<ref name="ref_ee8f29ef" /> | ||
+ | # 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).<ref name="ref_ccea9246">[https://gmd.copernicus.org/articles/12/613/2019/ Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets]</ref> | ||
+ | # Topological data analysis (TDA) aims to measure the “intrinsic shape” of data and identify this manifold despite noise and the likely nonlinear embedding.<ref name="ref_2466419d">[https://www.broadinstitute.org/talks/topological-data-analysis-what-persistent-homology Topological data analysis: What is persistent homology?]</ref> | ||
+ | # Topological data analysis reveals the structure of data.<ref name="ref_9e884c7e">[https://www.cambridge.org/core/books/topological-data-analysis-for-genomics-and-evolution/FCC8429FAD2B5D1525AEA47A8366D6EB Topological Data Analysis for Genomics and Evolution]</ref> | ||
+ | ===소스=== | ||
+ | <references /> |
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
- ↑ Topological Data Analysis Team
- ↑ What Is Topological Data Analysis? Data Defined
- ↑ Topological data analysis in digital marketing
- ↑ 4.0 4.1 4.2 Online Learning Series
- ↑ Topological Data Analysis and Beyond
- ↑ 6.0 6.1 6.2 Topological data analysis
- ↑ Topological Data Analysis of Vascular Disease: A Theoretical Framework
- ↑ A Mathematician's Perspective on Topological Data Analysis and R
- ↑ 9.0 9.1 9.2 Topological Data Analysis — Unpacking the Buzzword
- ↑ 10.0 10.1 Topological Data Analysis for Genomics and Evolution
- ↑ Topological data analysis of zebrafish patterns
메타데이터
위키데이터
- ID : Q4460773
Spacy 패턴 목록
- [{'LOWER': 'topological'}, {'LOWER': 'data'}, {'LEMMA': 'analysis'}]
노트
말뭉치
- On this page I have a number of items to get the interested reader started with persistent homology and topological data analysis (TDA).[1]
- 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]
- In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology.[3]
- 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]
- Statistical topological data analysis using persistence landscapes.[5]
- 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]
- 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]
- Our primary goal is to demonstrate the utility of topological data analysis for biological aggregations and similar applications.[6]
- We combine topological data analysis and machine learning to provide a collection of summary statistics describing patterns on both microscopic and macroscopic scales.[7]
- 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]
- 27, topological data analysis (TDA) has emerged as a valuable tool for characterizing collective behavior and self-organization.[7]
- The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA).[8]
- But around this same time I kept hearing about an exciting but possibly over-hyped topic called topological data analysis: TDA.[9]
- One of the key messages around topological data analysis is that data has shape and the shape matters.[10]
- What is topological data analysis (TDA), and why is TDA taking the big data world by storm?[11]
- 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]
- 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]
- 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]
- The development of this software will enable researchers at the Turing and elsewhere to apply topological data analysis at a previously unachievable scale.[14]
- 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]
- 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]
- Topological data analysis (TDA) aims to measure the “intrinsic shape” of data and identify this manifold despite noise and the likely nonlinear embedding.[16]
- Topological data analysis reveals the structure of data.[17]
소스
- ↑ Topological Data Analysis
- ↑ Topological Data Analysis • IMSI
- ↑ Topological data analysis
- ↑ An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists
- ↑ A User’s Guide to Topological Data Analysis
- ↑ 6.0 6.1 6.2 Topological Data Analysis of Biological Aggregation Models
- ↑ 7.0 7.1 7.2 Topological data analysis of zebrafish patterns
- ↑ Topological Data Analysis and Beyond
- ↑ A Mathematician's Perspective on Topological Data Analysis and R
- ↑ What Is Topological Data Analysis? Data Defined
- ↑ Introduction to Topological Data Analysis
- ↑ Journal of machine learning research 16 (2015) 77-102
- ↑ Topological data analysis can help predict crashes
- ↑ 14.0 14.1 14.2 Scalable topological data analysis
- ↑ Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
- ↑ Topological data analysis: What is persistent homology?
- ↑ Topological Data Analysis for Genomics and Evolution