A New Variational Framework for Rigid-Body Alignment

Kato, T; Tsuda, K; Tomii, K; Asai, K

doi:10.1007/978-3-540-27868-9_17

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

A New Variational Framework for Rigid-Body Alignment

MPS-Authors

/persons/resource/persons84265

Tsuda, K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://link.springer.com/content/pdf/10.1007%2F978-3-540-27868-9_17.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Kato, T., Tsuda, K., Tomii, K., & Asai, K. (2004). A New Variational Framework for Rigid-Body Alignment. In A. Fred, T. Caelli, R. Duin, A. Campilho, & D. de Ridder (Eds.), Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshops, SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 (pp. 171-179). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F3AD-A

Abstract

We present a novel algorithm for estimating the rigid-body transformation of a sequence of coordinates, aiming at the application to protein structures. Basically the sequence is modeled as a hidden Markov model where each state outputs an ellipsoidal Gaussian. Since maximum likelihood estimation requires to solve a complicated optimization problem, we introduce a variational estimation technique, which performs singular value decomposition in each step. Our probabilistic algorithm allows to superimpose a number of sequences which are rotated and translated in arbitrary ways.