Order Determination in Second-Order Source Separation Models Using Data Augmentation
Abstract
We propose a robust estimator for the number of latent components in an internal noise model within the second-order source separation (SOS) framework. Our approach utilizes a data augmentation strategy in conjunction with the robust SOS approach eSAM-AMUSE, which combines information from eigenvalues and variations of eigenvectors of eSAM-AMUSE. The resulting dimension estimate can be visualized using a ladle plot. Through a simulation study, we demonstrate the superior properties of the new estimator, which outperforms the bootstrap-based AMUSEladle estimator.
Main Authors
Format
Conferences
Conference paper
Published
2024
Series
Subjects
Publication in research information system
Publisher
Springer
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202408215602Use this for linking
Parent publication ISBN
978-3-031-65992-8
Review status
Peer reviewed
ISSN
2194-5357
DOI
https://doi.org/10.1007/978-3-031-65993-5_46
Conference
International Conference on Soft Methods in Probability and Statistics
Language
English
Published in
Advances in Intelligent Systems and Computing
Is part of publication
Combining, Modelling and Analyzing Imprecision, Randomness and Dependence
Citation
- Radojičić, U., & Nordhausen, K. (2024). Order Determination in Second-Order Source Separation Models Using Data Augmentation. In J. Ansari, S. Fuchs, W. Trutschnig, M. Asunción Lubiano, M. Ángeles Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), Combining, Modelling and Analyzing Imprecision, Randomness and Dependence (pp. 371-379). Springer. Advances in Intelligent Systems and Computing, 1458. https://doi.org/10.1007/978-3-031-65993-5_46
License
Additional information about funding
The work of Una Radojičić is supported by the Austrian Science Foundation, project number I 5799-N.
The record contains restricted files. Request a copy from the author.
Copyright© 2024 the Authors