J
Jie Chen
Researcher at Georgia Institute of Technology
Publications - 5
Citations - 959
Jie Chen is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Sparse approximation & Nonparametric statistics. The author has an hindex of 4, co-authored 5 publications receiving 919 citations. Previous affiliations of Jie Chen include Bank of America.
Papers
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Journal ArticleDOI
Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
Jie Chen,Xiaoming Huo +1 more
TL;DR: Simulations show that the predictions made by the proved theorems tend to be very conservative; this is consistent with some recent advances in probabilistic analysis based on random matrix theory.
Proceedings ArticleDOI
Sparse representations for multiple measurement vectors (MMV) in an over-complete dictionary
Jie Chen,Xiaoming Huo +1 more
TL;DR: The theoretical results show the fundamental limitation on when a sparse representation is unique, and the relation between the solutions of /spl lscr//sub 0/-norm minimization and the solution of /Spl lscR//sub 1/- norm minimization indicates a computationally efficient approach to find a sparse representations.
Journal ArticleDOI
Electricity Price Curve Modeling and Forecasting by Manifold Learning
TL;DR: In this article, a nonparametric approach for the modeling and analysis of electricity price curves by applying the manifold learning methodology-locally linear embedding (LLE) is proposed to make short-term and medium-term price forecasts.
Journal ArticleDOI
Complexity of penalized likelihood estimation
Xiaoming Huo,Jie Chen +1 more
TL;DR: It is shown that for a class of penalty functions, finding the global optimizer in the penalized least-squares estimation is equivalent to the ‘exact cover by 3-sets’ problem, which belongs to aclass of NP-hard problems.
Journal ArticleDOI
A Hessian Regularized Nonlinear Time Series Model
Jie Chen,Xiaoming Huo +1 more
TL;DR: The novel idea is to fit a model via penalization, where the penalty term is an unbiased estimator of the integrated Hessian of the underlying function, which is very general: it has Hessian almost everywhere in its domain.