DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, Ji Hyung | ko |
dc.contributor.author | Chung, Hye Won | ko |
dc.contributor.author | Lee, Ji Oon | ko |
dc.date.accessioned | 2021-07-23T01:10:17Z | - |
dc.date.available | 2021-07-23T01:10:17Z | - |
dc.date.created | 2021-07-16 | - |
dc.date.created | 2021-07-16 | - |
dc.date.created | 2021-07-16 | - |
dc.date.created | 2021-07-16 | - |
dc.date.created | 2021-07-16 | - |
dc.date.issued | 2021-07-22 | - |
dc.identifier.citation | Thirty-eighth International Conference on Machine Learning (ICML) | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | http://hdl.handle.net/10203/286835 | - |
dc.description.abstract | We 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.language | English | - |
dc.publisher | ICML committee | - |
dc.title | Detection of Signal in the Spiked Rectangular Models | - |
dc.type | Conference | - |
dc.identifier.wosid | 000683104605018 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | Thirty-eighth International Conference on Machine Learning (ICML) | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.contributor.localauthor | Chung, Hye Won | - |
dc.contributor.localauthor | Lee, Ji Oon | - |
dc.contributor.nonIdAuthor | Jung, Ji Hyung | - |
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