J
Jie Shen
Researcher at Stevens Institute of Technology
Publications - 67
Citations - 1343
Jie Shen is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Matrix completion & Rank (linear algebra). The author has an hindex of 16, co-authored 57 publications receiving 1127 citations. Previous affiliations of Jie Shen include Rutgers University & Shanghai Jiao Tong University.
Papers
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Representativeness of the Patient-Reported Outcomes Measurement Information System Internet panel.
TL;DR: Self-rated health of the PROMIS general population is similar to that of existing samples from the general U.S. population, and the findings suggest that the representativeness of the Internet data is comparable to those from probability-based general population samples.
Journal ArticleDOI
Racial/Ethnic disparities in ART adherence in the United States: findings from the MACH14 study.
Jane M. Simoni,David Huh,Ira B. Wilson,Jie Shen,Kathy Goggin,Nancy R. Reynolds,Robert H. Remien,Marc I. Rosen,David R. Bangsberg,Honghu Liu +9 more
TL;DR: Racial/ethnic differences in demographics, depression, and substance abuse do not explain the lower level of antiretroviral therapy adherence in African Americans observed in the sample and further research is needed to explain the persistent disparity.
Journal ArticleDOI
Calibration of self-reported oral health to clinically determined standards.
TL;DR: The model developed can be used to calibrate and adjust self-reported oral health status to that of clinically determined standards and for oral health screening of large populations in federal, state, and local programs, enabling great savings in resources used in dental care.
Posted Content
A Tight Bound of Hard Thresholding
TL;DR: A novel stochastic algorithm is presented which performs hard thresholding in each iteration, hence ensuring such parsimonious solutions and proves the {\em global linear convergence} for a number of prevalent statistical models under mild assumptions, even though the problem turns out to be non-convex.
Proceedings Article
Online low-rank subspace clustering by basis dictionary pursuit
TL;DR: In this article, a novel online implementation of low-rank representation (LRR) was proposed, which reduces the memory cost from O(n2) to O(pd, with p being the ambient dimension and d being some estimated rank (d ≤ p ≪ n).