Titlebook: An Introduction to Laplacian Spectral Distances and Kernels; Theory, Computation, Giuseppe Patanè Book 2017 Springer Nature Switzerland AG

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書目名稱An Introduction to Laplacian Spectral Distances and Kernels影響因子(影響力)




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書目名稱An Introduction to Laplacian Spectral Distances and Kernels讀者反饋




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連系

2469-4215 he reviewed numerical schemes are discussed and compared in terms of robustness, approximation accuracy, and computational cost, thus supporting the reader in t978-3-031-01465-9978-3-031-02593-8Series ISSN 2469-4215 Series E-ISSN 2469-4223

remission

Berechnung des Energieverbrauchesresponding saddles. In a similar way, Laplacian eigenfunctions associated with small eigenvalues and diffusion maps at small scales are characterized by a generally low number of critical points, whose spatial location cannot be defined ..

alliance

An Introduction to Laplacian Spectral Distances and KernelsTheory, Computation,

lipoatrophy

Book 2017cian spectrum. As main applications, we discuss the design of smooth functions and the Laplacian smoothing of noisy scalar functions...All the reviewed numerical schemes are discussed and compared in terms of robustness, approximation accuracy, and computational cost, thus supporting the reader in t

Antimicrobial

2469-4215 s and distances, such as commute time, biharmonic, diffusion, and wave distances...Within this context, this book is intended to provide a common background on the definition and computation of the Laplacian spectral kernels and distances for geometry processing and shape analysis. To this end, we d

極大的痛苦

MILK

Conclusions,st appropriate with respect to shape representation, computational resources, and target application, all the reviewed numerical schemes have been discussed and compared in terms of robustness, approximation accuracy, and computational cost.

跑過(guò)

Book 2017ances, such as commute time, biharmonic, diffusion, and wave distances...Within this context, this book is intended to provide a common background on the definition and computation of the Laplacian spectral kernels and distances for geometry processing and shape analysis. To this end, we define a un

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