To refer to this page use:
http://arks.princeton.edu/ark:/88435/pr1gx5v
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Anden, Joakim | - |
dc.contributor.author | Singer, Amit | - |
dc.date.accessioned | 2019-08-29T17:01:58Z | - |
dc.date.available | 2019-08-29T17:01:58Z | - |
dc.date.issued | 2017 | en_US |
dc.identifier.citation | Anden, Joakim, Singer, Amit. (2017). Factor Analysis for Spectral Estimation. 2017 INTERNATIONAL CONFERENCE ON SAMPLING THEORY AND APPLICATIONS (SAMPTA), 169 - 173 | en_US |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr1gx5v | - |
dc.description.abstract | Power spectrum estimation is an important tool in many applications, such as the whitening of noise. The popular multitaper method enjoys significant success, but fails for short signals with few samples. We propose a statistical model where a signal is given by a random linear combination of fixed, yet unknown, stochastic sources. Given multiple such signals, we estimate the subspace spanned by the power spectra of these fixed sources. Projecting individual power spectrum estimates onto this subspace increases estimation accuracy. We provide accuracy guarantees for this method and demonstrate it on simulated and experimental data from cryo-electron microscopy. | en_US |
dc.format.extent | 169 - 173 | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartof | 2017 INTERNATIONAL CONFERENCE ON SAMPLING THEORY AND APPLICATIONS (SAMPTA) | en_US |
dc.rights | Author's manuscript | en_US |
dc.title | Factor Analysis for Spectral Estimation | en_US |
dc.type | Conference Article | en_US |
dc.date.eissued | 2017-09-04 | en_US |
pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/journal-article | en_US |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1702.04672v2.pdf | 402.61 kB | Adobe PDF | View/Download |
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.