Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Perros, Ioakeim"'
Autor:
Afshar, Ardavan, Perros, Ioakeim, Park, Haesun, deFilippi, Christopher, Yan, Xiaowei, Stewart, Walter, Ho, Joyce, Sun, Jimeng
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective to
Externí odkaz:
http://arxiv.org/abs/1911.05843
Autor:
Perros, Ioakeim, Papalexakis, Evangelos E., Park, Haesun, Vuduc, Richard, Yan, Xiaowei, Defilippi, Christopher, Stewart, Walter F., Sun, Jimeng
This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. Such data are common when the values correspond to event counts or ordinal measures. The conventiona
Externí odkaz:
http://arxiv.org/abs/1803.05473
Autor:
Afshar, Ardavan, Perros, Ioakeim, Papalexakis, Evangelos E., Searles, Elizabeth, Ho, Joyce, Sun, Jimeng
PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set of patients with the varying number of medical encounters over time. De
Externí odkaz:
http://arxiv.org/abs/1803.04572
Autor:
Perros, Ioakeim, Papalexakis, Evangelos E., Wang, Fei, Vuduc, Richard, Searles, Elizabeth, Thompson, Michael, Sun, Jimeng
In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of t
Externí odkaz:
http://arxiv.org/abs/1703.04219
We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its namesake, the classical Hierarchical Tucker method, which aims to comput
Externí odkaz:
http://arxiv.org/abs/1610.07722
Publikováno v:
In Journal of Biomedical Informatics January 2020 101
Autor:
Huang, Furong, N., Niranjan U., Perros, Ioakeim, Chen, Robert, Sun, Jimeng, Anandkumar, Anima
We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a "divide-and-conquer" strategy that learns models over small groups of variables and iteratively merges onto a glo
Externí odkaz:
http://arxiv.org/abs/1406.4566
Publikováno v:
In Journal of Biomedical Informatics May 2019 93
Publikováno v:
2015 IEEE International Conference on Data Mining; 2015, p943-948, 6p
Autor:
Perros Ioakeim
Μη διαθέσιμη περίληψη Not available summarization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4037::1abb1c153a1bab4c54d71bdfc5fd188f
http://purl.tuc.gr/dl/dias/A889B49C-6D12-4F69-A915-C40CB97549C1
http://purl.tuc.gr/dl/dias/A889B49C-6D12-4F69-A915-C40CB97549C1