# Factor Analysis for Spectral Estimation

## Author(s): Anden, Joakim; Singer, Amit

To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1gx5v
DC FieldValueLanguage
dc.contributor.authorAnden, Joakim-
dc.contributor.authorSinger, Amit-
dc.date.accessioned2019-08-29T17:01:58Z-
dc.date.available2019-08-29T17:01:58Z-
dc.date.issued2017en_US
dc.identifier.citationAnden, Joakim, Singer, Amit. (2017). Factor Analysis for Spectral Estimation. 2017 INTERNATIONAL CONFERENCE ON SAMPLING THEORY AND APPLICATIONS (SAMPTA), 169 - 173en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1gx5v-
dc.description.abstractPower 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.extent169 - 173en_US
dc.language.isoen_USen_US
dc.relation.ispartof2017 INTERNATIONAL CONFERENCE ON SAMPLING THEORY AND APPLICATIONS (SAMPTA)en_US
dc.rightsAuthor's manuscripten_US
dc.titleFactor Analysis for Spectral Estimationen_US
dc.typeConference Articleen_US
dc.date.eissued2017-09-04en_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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