Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification
Given a row-stochastic matrix describing pairwise similarities between data objects, spectral clustering makes use of the eigenvectors of this matrix to perform dimensionality reduction for clustering in fewer dimensions. One example from this class of algorithms is the Robust Perron Cluster Analysis (PCCA+), which delivers a fuzzy clustering. Originally developed for clustering the state space of Markov chains, the method became popular as a versatile tool for general data classification problems. The robustness of PCCA+, however, cannot be explained by previous perturbation results, because the matrices in typical applications do not comply with the two main requirements: reversibility and nearly decomposability. We therefore demonstrate in this paper that PCCA+ always delivers an optimal fuzzy clustering for nearly uncoupled, not necessarily reversible, Markov chains with transition states. Copyright Springer-Verlag Berlin Heidelberg 2013
Year of publication: |
2013
|
---|---|
Authors: | Röblitz, Susanna ; Weber, Marcus |
Published in: |
Advances in Data Analysis and Classification. - Springer. - Vol. 7.2013, 2, p. 147-179
|
Publisher: |
Springer |
Subject: | Perron eigenvalues | Perturbation theory | Molecular simulations |
Saved in:
Online Resource
Saved in favorites
Similar items by subject
-
Lechuga-Sanabria, Fidelmar, (2013)
-
Escobar, Marcos, (2014)
-
Pricing two-asset barrier options under stochastic correlation via perturbation
Escobar, Marcos, (2015)
- More ...
Similar items by person
-
BETRIEBSWIRTSCHAFT - Basel II Rahmenwerk: Die Risiken der Projektfinanzierung
Schöning, Stephan, (2005)
- More ...