Jinchun Zhan
Jinchun Zhan
PhD of Electrical Engineering, Iowa State University
Verified email at alumni.iastate.edu
Cited by
Cited by
Handbook of robust low-rank and sparse matrix decomposition: Applications in image and video processing
T Bouwmans, NS Aybat, E Zahzah
CRC Press, 2016
Recursive recovery of sparse signal sequences from compressive measurements: A review
N Vaswani, J Zhan
IEEE Transactions on Signal Processing 64 (13), 3523-3549, 2016
Online (and offline) robust PCA: Novel algorithms and performance guarantees
J Zhan, B Lois, H Guo, N Vaswani
Artificial intelligence and statistics, 1488-1496, 2016
Robust PCA with partial subspace knowledge
J Zhan, N Vaswani
IEEE Transactions on Signal Processing 63 (13), 3332-3347, 2015
Time invariant error bounds for modified-cs-based sparse signal sequence recovery
J Zhan, N Vaswani
IEEE Transactions on Information Theory 61 (3), 1389-1409, 2015
What is the best method to fit time-resolved data? A comparison of the residual minimization and the maximum likelihood techniques as applied to experimental time-correlated …
K Santra, J Zhan, X Song, EA Smith, N Vaswani, JW Petrich
The Journal of Physical Chemistry B 120 (9), 2484-2490, 2016
Performance guarantees for reprocs-correlated low-rank matrix entries case
J Zhan, N Vaswani, C Qiu
2014 IEEE International Symposium on Information Theory, 2187-2191, 2014
Separating sparse and low-dimensional signal sequences from time-varying undersampled projections of their sums
J Zhan, N Vaswani, I Atkinson
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
Recursive Dynamic CS: Recursive Recovery of Sparse Signal Sequences from Compressive Measurements: A Review
N Vaswani, J Zhan
(submitted to) IEEE Transactions on Signal Processing, 2015
Online (Recursive) Robust Principal Components Analysis
N Vaswani, C Qiu, B Lois, H Guo, J Zhan
Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in …, 2016
Sparse and low rank signal recovery with partial knowledge
J Zhan
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