• Produktbild: Near Extensions and Alignment of Data in R(superscript)N
  • Produktbild: Near Extensions and Alignment of Data in R(superscript)N

Near Extensions and Alignment of Data in R(superscript)N Whitney Extensions of Near Isometries, Shortest Paths, Equidistribution, Clustering and Non-rigid Alignment of data in Euclidean space

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

20.11.2023

Verlag

John Wiley & Sons Inc

Seitenzahl

192

Maße (L/B/H)

23,5/15,7/1,5 cm

Gewicht

425 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-394-19677-7

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

20.11.2023

Verlag

John Wiley & Sons Inc

Seitenzahl

192

Maße (L/B/H)

23,5/15,7/1,5 cm

Gewicht

425 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-394-19677-7

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Near Extensions and Alignment of Data in R(superscript)N
  • Produktbild: Near Extensions and Alignment of Data in R(superscript)N
  • Preface xiii

    Overview xvii

    Structure xix

    1 Variants 1-2 1

    1.1 The Whitney Extension Problem 1

    1.2 Variants (1-2) 1

    1.3 Variant 2 2

    1.4 Visual Object Recognition and an Equivalence Problem in Rd 3

    1.5 Procrustes: The Rigid Alignment Problem 4

    1.6 Non-rigid Alignment 6

    2 Building ¿-distortions: Slow Twists, Slides 9

    2.1 c-distorted Diffeomorphisms 9

    2.2 Slow Twists 10

    2.3 Slides 11

    2.4 Slow Twists: Action 11

    2.5 Fast Twists 13

    2.6 Iterated Slow Twists 15

    2.7 Slides: Action 15

    2.8 Slides at Different Distances 18

    2.9 3D Motions 20

    2.10 3D Slides 21

    2.11 Slow Twists and Slides: Theorem 2.1 23

    2.12 Theorem 2.2 23

    3 Counterexample to Theorem 2.2 (part (1)) for card (E)> d 25

    3.1 Theorem 2.2 (part (1)), Counterexample: k > d 25

    3.2 Removing the Barrier k > d in Theorem 2.2 (part (1)) 27

    4 Manifold Learning, Near-isometric Embeddings, Compressed Sensing, Johnson-Lindenstrauss and Some Applications Related to the near Whitney extension problem 29

    4.1 Manifold and Deep Learning Via c-distorted Diffeomorphisms 29

    4.2 Near Isometric Embeddings, Compressive Sensing, Johnson-Lindenstrauss and Applications Related to c-distorted Diffeomorphisms 30

    4.3 Restricted Isometry 31

    5 Clusters and Partitions 33

    5.1 Clusters and Partitions 33

    5.2 Similarity Kernels and Group Invariance 34

    5.3 Continuum Limits of Shortest Paths Through Random Points and Shortest Path Clustering 35

    5.3.1 Continuum Limits of Shortest Paths Through Random Points: The Observation 35

    5.3.2 Continuum Limits of Shortest Paths Through Random Points: The Set Up 36

    5.4 Theorem 5.6 37

    5.5 p-power Weighted Shortest Path Distance and Longest-leg Path Distance 37

    5.6 p-wspm, Well Separation Algorithm Fusion 38

    5.7 Hierarchical Clustering in Rd 39

    6 The Proof of Theorem 2.3 41

    6.1 Proof of Theorem 2.3 (part(2)) 41

    6.2 A Special Case of the Proof of Theorem 2.3 (part (1)) 42

    6.3 The Remaining Proof of Theorem 2.3 (part (1)) 45

    7 Tensors, Hyperplanes, Near Reflections, Constants (¿, ¿, K) 51

    7.1 Hyperplane; We Meet the Positive Constant ¿ 51

    7.2 "Well Separated"; We Meet the Positive Constant ¿ 52

    7.3 Upper Bound for Card (E); We Meet the Positive Constant K 52

    7.4 Theorem 7.11 52

    7.5 Near Reflections 52

    7.6 Tensors, Wedge Product, and Tensor Product 53

    8 Algebraic Geometry: Approximation-varieties, Lojasiewicz, Quantification: (¿, ¿)-Theorem 2.2 (part (2)) 55

    8.1 Min-max Optimization and Approximation-varieties 56

    8.2 Min-max Optimization and Convexity 57

    9 Building ¿-distortions: Near Reflections 59

    9.1 Theorem 9.14 59

    9.2 Proof of Theorem 9.14 59

    10 ¿-distorted diffeomorphisms, O(d) and Functions of Bounded Mean Oscillation (BMO) 61

    10.1 Bmo 61

    10.2 The John-Nirenberg Inequality 62

    10.3 Main Results 62

    10.4 Proof of Theorem 10.17 63

    10.5 Proof of Theorem 10.18 66

    10.6 Proof of Theorem 10.19 66

    10.7 An Overdetermined System 67

    10.8 Proof of Theorem 10.16 70

    11 Results: A Revisit of Theorem 2.2 (part (1)) 71

    11.1 Theorem 11.21 71

    11.2 ¿ blocks 74

    11.3 Finiteness Principle 76

    12 Proofs: Gluing and Whitney Machinery 77

    12.1 Theorem 11.23 77

    12.2 The Gluing Theorem 78

    12.3 Hierarchical Clusterings of Finite Subsets of Rd Revisited 81

    12.4 Proofs of Theorem 11.27 and Theorem 11.28 82

    12.5 Proofs of Theorem 11.31, Theorem 11.30 and Theorem 11.29 86

    13 Extensions of Smooth Small Distortions [41]: Introduction 89

    13.1 Class of Sets E 89

    13.2 Main Result 89

    14 Extensions of Smooth Small Distortions: First Results 91

    Lemma 14.1 91

    Lemma 14.2 92

    Lemma 14.3 92

    Lemma 14.4 93

    Lemma 14.5 93

    15 Extensions of Smooth Small Distortions: Cubes, Partitions of Unity, Whitney Machinery 95

    15.1 Cubes 95

    15.2 Partition of Unity 95

    15.3 Regularized Distance 95

    16 Extensions of Smooth Small Distortions: Picking Motions 99

    Lemma 16.1 99

    Lemma 16.2 101

    17 Extensions of Smooth Small Distortions: Unity Partitions 103

    18 Extensions of Smooth Small Distortions: Function Extension 105

    Lemma 18.1 105

    Lemma 18.2 106

    19 Equidistribution: Extremal Newtonian-like Configurations, Group Invariant Discrepancy, Finite Fields, Combinatorial Designs, Linear Independent Vectors, Matroids and the Maximum Distance Separable Conjecture 109

    19.1 s-extremal Configurations and Newtonian s-energy 109

    19.2 [¿1, 1] 110

    19.2.1 Critical Transition 110

    19.2.2 Distribution of s-extremal Configurations 111

    19.2.3 Equally Spaced Points for Interpolation 112

    19.3 The n-dimensional Sphere, Sn Embedded in Rn + 1 112

    19.3.1 Critical Transition 112

    19.4 Torus 113

    19.5 Separation Radius and Mesh Norm for s-extremal Configurations 114

    19.5.1 Separation Radius of s > n-extremal Configurations on a Set Yn 116

    19.5.2 Separation Radius of s < n ¿ 1-extremal Configurations on Sn 116

    19.5.3 Mesh Norm of s-extremal Configurations on a Set Yn 116

    19.6 Discrepancy of Measures, Group Invariance 117

    19.7 Finite Field Algorithm 119

    19.7.1 Examples 120

    19.7.2 Spherical ¿t-designs 120

    19.7.3 Extension to Finite Fields of Odd Prime Powers 121

    19.8 Combinatorial Designs, Linearly Independent Vectors, MDS Conjecture 121

    19.8.1 The Case q = 2 122

    19.8.2 The General Case 122

    19.8.3 The Maximum Distance Separable Conjecture 123

    20 Covering of SU(2) and Quantum Lattices 125

    20.1 Structure of SU(2) 126

    20.2 Universal Sets 127

    20.3 Covering Exponent 128

    20.4 An Efficient Universal Set in PSU(2) 128

    21 The Unlabeled Correspondence Configuration Problem and Optimal Transport 131

    21.1 Unlabeled Correspondence Configuration Problem 131

    21.1.1 Non-reconstructible Configurations 131

    21.1.2 Example 132

    21.1.3 Partition Into Polygons 134

    21.1.4 Considering Areas of Triangles-10-step Algorithm 134

    21.1.5 Graph Point of View 137

    21.1.6 Considering Areas of Quadrilaterals 137

    21.1.7 Partition Into Polygons for Small Distorted Pairwise Distances 138

    21.1.8 Areas of Triangles for Small Distorted Pairwise Distances 138

    21.1.9 Considering Areas of Triangles (part 2) 141

    21.1.10 Areas of Quadrilaterals for Small Distorted Pairwise Distances 142

    21.1.11 Considering Areas of Quadrilaterals (part 2) 145

    22 A Short Section on Optimal Transport 147

    23 Conclusion 149

    References 151

    Index 159