Detection of Signal in the Spiked Rectangular Models

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dc.contributor.authorJung, Ji Hyungko
dc.contributor.authorChung, Hye Wonko
dc.contributor.authorLee, Ji Oonko
dc.date.accessioned2021-07-23T01:10:17Z-
dc.date.available2021-07-23T01:10:17Z-
dc.date.created2021-07-16-
dc.date.created2021-07-16-
dc.date.created2021-07-16-
dc.date.created2021-07-16-
dc.date.created2021-07-16-
dc.date.issued2021-07-22-
dc.identifier.citationThirty-eighth International Conference on Machine Learning (ICML)-
dc.identifier.issn2640-3498-
dc.identifier.urihttp://hdl.handle.net/10203/286835-
dc.description.abstractWe consider the problem of detecting signals in the rank-one signal-plus-noise data matrix models that generalize the spiked Wishart matrices. We show that the principal component analysis can be improved by pre-transforming the matrix entries if the noise is non-Gaussian. As an intermediate step, we prove a sharp phase transition of the largest eigenvalues of spiked rectangular matrices, which extends the Baik-Ben Arous-P\'ech\'e (BBP) transition. We also propose a hypothesis test to detect the presence of signal with low computational complexity, based on the linear spectral statistics, which minimizes the sum of the Type-I and Type-II errors when the noise is Gaussian.-
dc.languageEnglish-
dc.publisherICML committee-
dc.titleDetection of Signal in the Spiked Rectangular Models-
dc.typeConference-
dc.identifier.wosid000683104605018-
dc.type.rimsCONF-
dc.citation.publicationnameThirty-eighth International Conference on Machine Learning (ICML)-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationVirtual-
dc.contributor.localauthorChung, Hye Won-
dc.contributor.localauthorLee, Ji Oon-
dc.contributor.nonIdAuthorJung, Ji Hyung-
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EE-Conference Papers(학술회의논문)MA-Conference Papers(학술회의논문)
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