Tangential Fixpoint Iterations for Gromov-Wasserstein Barycenters\ast

被引:0
作者
Beier, Florian [1 ]
Beinert, Robert [1 ]
机构
[1] Tech Univ Berlin, Inst Math, D-10623 Berlin, Germany
关键词
Gromov-Wasserstein barycenters; FREchet mean iterations; Gromov-Wasserstein tangent spaces; MAXIMIZING ACCURACY; GEOMETRY;
D O I
10.1137/24M1654804
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Gromov-Wasserstein (GW) transport problem is a generalization of classic optimal transport, which seeks a transport between two measures while preserving their internal geometry. Due to meeting this theoretical underpinning, it is a valuable tool for the analysis of objects that do not possess a natural embedding or should be studied independently of it. Prime applications can thus be found in, e.g., shape matching, classification, and interpolation tasks. To tackle the latter, one theoretically justified approach is the employment of multimarginal GW transport and GW barycenters, which are Fre'\chet means with respect to the GW distance. However, because the computation of GW itself already poses a quadratic and nonconvex optimization problem, the determination of GW barycenters is a hard task, and algorithms for their computation are scarce. In this paper, we revisit a known procedure for the determination of Fre'\chet means in Riemannian manifolds via tangential approximations in the context of GW. We provide a characterization of barycenters in the GW tangent space, which ultimately gives rise to a fixpoint iteration for approximating GW barycenters using multimarginal plans. We propose a relaxation of this fixpoint iteration and show that it monotonously decreases the barycenter loss. In certain cases our proposed method naturally provides us with barycentric embeddings. The resulting algorithm is capable of producing qualitative shape interpolations between multiple 3D shapes with support sizes of over thousands of points in reasonable time. In addition, we verify our method on shape classification and multigraph matching tasks.
引用
收藏
页码:1058 / 1100
页数:43
相关论文
共 69 条
[1]   BARYCENTERS IN THE WASSERSTEIN SPACE [J].
Agueh, Martial ;
Carlier, Guillaume .
SIAM JOURNAL ON MATHEMATICAL ANALYSIS, 2011, 43 (02) :904-924
[2]  
Altekrüger F, 2023, PR MACH LEARN RES, V202, P664
[3]   Hardness results for Multimarginal Optimal Transport problems [J].
Altschuler, Jason M. ;
Boix-Adsera, Enric .
DISCRETE OPTIMIZATION, 2021, 42
[4]   A fixed-point approach to barycenters in Wasserstein space [J].
Alvarez-Esteban, Pedro C. ;
del Barrio, E. ;
Cuesta-Albertos, J. A. ;
Matran, C. .
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 2016, 441 (02) :744-762
[5]  
Alvarez-Melis D, 2018, 2018 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2018), P1881
[6]  
Ambrosio L., 2021, LECT OPTIMAL TRANSPO, V130, DOI DOI 10.1007/978-3-030-72162-6
[7]  
Ambrosio L, 2008, LECT MATH, P1
[8]  
Arbel M, 2019, ADV NEUR IN, V32
[9]  
Arjovsky M, 2017, PR MACH LEARN RES, V70
[10]  
Beier Florian, 2023, Scale Space and Variational Methods in Computer Vision: 9th International Conference, SSVM 2023, Proceedings. Lecture Notes in Computer Science (14009), P614, DOI 10.1007/978-3-031-31975-4_47