activity
20122017
most citedThe spectral symmetry of weakly irreducible nonnegative tensors and connected hypergraphs

35 citations · 66 across the 6 of their papers we have counts for

collaborators

6 papers

math.CO2017

The Dimension of Eigenvariety of Nonnegative Tensors Associated with Spectral Radius

Yi-Zheng Fan, Tao Huang, Yan-Hong Bao

For a nonnegative weakly irreducible tensor, its spectral radius is an eigenvalue corresponding to a unique positive eigenvector up to a scalar called the Perron vector. But includ…

math.CO2017★ 20 cited

Eigenvariety of Nonnegative Symmetric Weakly Irreducible Tensors Associated with Spectral Radius and Its Application to Hypergraphs

Yi-Zheng Fan, Yan-Hong Bao, Tao Huang

For a nonnegative symmetric weakly irreducible tensor, its spectral radius is an eigenvalue corresponding to a unique positive eigenvector up to a scalar called the Perron vector.…

math.CO2017★ 35 cited

The spectral symmetry of weakly irreducible nonnegative tensors and connected hypergraphs

Yi-Zheng Fan, Tao Huang, Yan-Hong Bao +2

Let be a weakly irreducible nonnegative tensor with spectral radius . Let (respectively, ) be the set of normalized…

cs.IT2013★ 5 cited

Symmetric Toeplitz-Structured Compressed Sensing Matrices

Tao Huang, Yi-Zheng Fan, Ming Zhu

How to construct a suitable measurement matrix is still an open question in compressed sensing. A significant part of the recent work is that the measurement matrices are not compl…

cs.IT2013★ 1 cited

Mixed Compressed Sensing Based on Random Graphs

Yi-Zheng Fan, Tao Huang, Ming Zhu

Finding a suitable measurement matrix is an important topic in compressed sensing. Though the known random matrix, whose entries are drawn independently from a certain probability…

cs.IT2012★ 5 cited

Compressed Sensing Based on Random Symmetric Bernoulli Matrix

Yi-Zheng Fan, Tao Huang, Ming Zhu

The task of compressed sensing is to recover a sparse vector from a small number of linear and non-adaptive measurements, and the problem of finding a suitable measurement matrix i…