Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Anru R. Zhang"'
Publikováno v:
IEEE Transactions on Information Theory. 68:5975-6002
We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model -- an
Publikováno v:
Proceedings of the 55th Annual ACM Symposium on Theory of Computing.
Publikováno v:
IEEE Trans Inf Theory
This paper studies a general framework for high-order tensor SVD. We propose a new computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to estimate the low tensor-train rank structure from the noisy high-order tens
Autor:
Francesca Rigiroli, Jocelyn Hoye, Reginald Lerebours, Peijie Lyu, Kyle J. Lafata, Anru R. Zhang, Alaattin Erkanli, Niharika B. Mettu, Desiree E. Morgan, Ehsan Samei, Daniele Marin
Publikováno v:
European Radiology.
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
The Annals of Statistics. 50
High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex and discontinuous nature of this problem pose significant challenges in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20dfc99411a36fe02deaf1e240f5a44f
http://arxiv.org/abs/2012.09996
http://arxiv.org/abs/2012.09996
This paper describes a flexible framework for generalized low-rank tensor estimation problems that includes many important instances arising from applications in computational imaging, genomics, and network analysis. The proposed estimator consists o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db559c66a89720736b74963e9987389d
http://arxiv.org/abs/2002.11255
http://arxiv.org/abs/2002.11255
Autor:
Yuetian Luo, Anru R. Zhang
This paper studies the statistical and computational limits of high-order clustering with planted structures. We focus on two clustering models, constant high-order clustering (CHC) and rank-one higher-order clustering (ROHC), and study the methods a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d51280b78595c3168ae67a2f5a83028d