Near extensions and alignment of data in R^n: Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space
Near Extensions and Alignment of Data in Rn Comprehensive resource illustrating the mathematical richness of Whitney Extension Problems, enabling readers to develop new insights, tools, and mathematical techniques Near Extensions and Alignment of Data in Rn demonstrates a range of hitherto unknown c...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
John Wiley & Johns
2024
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781394196777/?ar |
| Zusammenfassung: | Near Extensions and Alignment of Data in Rn Comprehensive resource illustrating the mathematical richness of Whitney Extension Problems, enabling readers to develop new insights, tools, and mathematical techniques Near Extensions and Alignment of Data in Rn demonstrates a range of hitherto unknown connections between current research problems in engineering, mathematics, and data science, exploring the mathematical richness of near Whitney Extension Problems, and presenting a new nexus of applied, pure and computational harmonic analysis, approximation theory, data science, and real algebraic geometry. For example, the book uncovers connections between near Whitney Extension Problems and the problem of alignment of data in Euclidean space, an area of considerable interest in computer vision. Written by a highly qualified author, Near Extensions and Alignment of Data in Rn includes information on: Areas of mathematics and statistics, such as harmonic analysis, functional analysis, and approximation theory, that have driven significant advances in the field Development of algorithms to enable the processing and analysis of huge amounts of data and data sets Why and how the mathematical underpinning of many current data science tools needs to be better developed to be useful New insights, potential tools, and mathematical techniques to solve problems in Whitney extensions, signal processing, shortest paths, clustering, computer vision, optimal transport, manifold learning, minimal energy, and equidistribution Providing comprehensive coverage of several subjects, Near Extensions and Alignment of Data in Rn is an essential resource for mathematicians, applied mathematicians, and engineers working on problems related to data science, signal processing, computer vision, manifold learning, and optimal transport. |
| Beschreibung: | Includes bibliographical references and index. - Print version record |
| Umfang: | 1 Online-Ressource (224 Seiten) |
| ISBN: | 9781394196814 1394196814 9781394196777 |
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| spelling | Damelin, Steven B. VerfasserIn aut Near extensions and alignment of data in R^n Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space Steven B. Damelin Hoboken, NJ John Wiley & Johns 2024 1 Online-Ressource (224 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Print version record Near Extensions and Alignment of Data in Rn Comprehensive resource illustrating the mathematical richness of Whitney Extension Problems, enabling readers to develop new insights, tools, and mathematical techniques Near Extensions and Alignment of Data in Rn demonstrates a range of hitherto unknown connections between current research problems in engineering, mathematics, and data science, exploring the mathematical richness of near Whitney Extension Problems, and presenting a new nexus of applied, pure and computational harmonic analysis, approximation theory, data science, and real algebraic geometry. For example, the book uncovers connections between near Whitney Extension Problems and the problem of alignment of data in Euclidean space, an area of considerable interest in computer vision. Written by a highly qualified author, Near Extensions and Alignment of Data in Rn includes information on: Areas of mathematics and statistics, such as harmonic analysis, functional analysis, and approximation theory, that have driven significant advances in the field Development of algorithms to enable the processing and analysis of huge amounts of data and data sets Why and how the mathematical underpinning of many current data science tools needs to be better developed to be useful New insights, potential tools, and mathematical techniques to solve problems in Whitney extensions, signal processing, shortest paths, clustering, computer vision, optimal transport, manifold learning, minimal energy, and equidistribution Providing comprehensive coverage of several subjects, Near Extensions and Alignment of Data in Rn is an essential resource for mathematicians, applied mathematicians, and engineers working on problems related to data science, signal processing, computer vision, manifold learning, and optimal transport. Mathematical analysis Geometry, Analytic Rigidity (Geometry) Nomography (Mathematics) Euclidean algorithm Isometrics (Mathematics) Analyse mathématique Géométrie analytique Rigidité (Géométrie) Nomographie (Mathématiques) Algorithme d'Euclide Isométrie (Mathématiques) 9781394196777 Erscheint auch als Druck-Ausgabe 9781394196777 |
| spellingShingle | Damelin, Steven B. Near extensions and alignment of data in R^n Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space Mathematical analysis Geometry, Analytic Rigidity (Geometry) Nomography (Mathematics) Euclidean algorithm Isometrics (Mathematics) Analyse mathématique Géométrie analytique Rigidité (Géométrie) Nomographie (Mathématiques) Algorithme d'Euclide Isométrie (Mathématiques) |
| title | Near extensions and alignment of data in R^n Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space |
| title_auth | Near extensions and alignment of data in R^n Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space |
| title_exact_search | Near extensions and alignment of data in R^n Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space |
| title_full | Near extensions and alignment of data in R^n Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space Steven B. Damelin |
| title_fullStr | Near extensions and alignment of data in R^n Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space Steven B. Damelin |
| title_full_unstemmed | Near extensions and alignment of data in R^n Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space Steven B. Damelin |
| title_short | Near extensions and alignment of data in R^n |
| title_sort | near extensions and alignment of data in r n whitney extensions of near isometries shortest paths equidistribution clustering and non rigid alignment of data in euclidean space |
| title_sub | Whitney extensions of near isometries, shortest paths, equidistribution, clustering and non-rigid alignment of data in Euclidean space |
| topic | Mathematical analysis Geometry, Analytic Rigidity (Geometry) Nomography (Mathematics) Euclidean algorithm Isometrics (Mathematics) Analyse mathématique Géométrie analytique Rigidité (Géométrie) Nomographie (Mathématiques) Algorithme d'Euclide Isométrie (Mathématiques) |
| topic_facet | Mathematical analysis Geometry, Analytic Rigidity (Geometry) Nomography (Mathematics) Euclidean algorithm Isometrics (Mathematics) Analyse mathématique Géométrie analytique Rigidité (Géométrie) Nomographie (Mathématiques) Algorithme d'Euclide Isométrie (Mathématiques) |
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