155 citations · 888 across the 24 of their papers we have counts for
24 papers
Improved Convergence Factor of Windowed Anderson Acceleration for Symmetric Fixed-Point Iterations
Casey Garner, Gilad Lerman, Teng Zhang
This paper studies the commonly utilized windowed Anderson acceleration (AA) algorithm for fixed-point methods, . It provides the first proof that when the op…
Orthogonal Trace-Sum Maximization: Tightness of the Semidefinite Relaxation and Guarantee of Locally Optimal Solutions
Joong-Ho Won, Teng Zhang, Hua Zhou
This paper studies an optimization problem on the sum of traces of matrix quadratic forms in semi-orthogonal matrices, which can be considered as a generalization of the synchr…
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers
Honggang Yu, Shihfeng Zeng, Teng Zhang +2
Machine learning researchers have long noticed the phenomenon that the model training process will be more effective and efficient when the training samples are densely sampled aro…
Tightness of the semidefinite relaxation for orthogonal trace-sum maximization
Teng Zhang
This paper studies an optimization problem on the sum of traces of matrix quadratic forms on orthogonal matrices, which can be considered as a generalization of the synchroniza…
Element-wise estimation error of a total variation regularized estimator for change point detection
Teng Zhang
This work studies the total variation regularized estimator (fused lasso) in the setting of a change point detection problem. Compared with existing works that focus on th…
Phase Retrieval by Alternating Minimization with Random Initialization
Teng Zhang
We consider a phase retrieval problem, where the goal is to reconstruct a -dimensional complex vector from its phaseless scalar products with sensing vectors, independently…
Exact Camera Location Recovery by Least Unsquared Deviations
Gilad Lerman, Yunpeng Shi, Teng Zhang
We establish exact recovery for the Least Unsquared Deviations (LUD) algorithm of Ozyesil and Singer. More precisely, we show that for sufficiently many cameras with given corrupte…
Robust PCA by Manifold Optimization
Teng Zhang, Yi Yang
Robust PCA is a widely used statistical procedure to recover a underlying low-rank matrix with grossly corrupted observations. This work considers the problem of robust PCA as a no…
Phase retrieval using alternating minimization in a batch setting
Teng Zhang
This paper considers the problem of phase retrieval, where the goal is to recover a signal from the observations , . While many algorithms…
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery
Tyler Maunu, Teng Zhang, Gilad Lerman
We present a mathematical analysis of a non-convex energy landscape for robust subspace recovery. We prove that an underlying subspace is the only stationary point and local minimi…
Anisotropic twicing for single particle reconstruction using autocorrelation analysis
Tejal Bhamre, Teng Zhang, Amit Singer
The missing phase problem in X-ray crystallography is commonly solved using the technique of molecular replacement, which borrows phases from a previously solved homologous structu…
Denoising and Covariance Estimation of Single Particle Cryo-EM Images
Tejal Bhamre, Teng Zhang, Amit Singer
The problem of image restoration in cryo-EM entails correcting for the effects of the Contrast Transfer Function (CTF) and noise. Popular methods for image restoration include `pha…
Flexible Expectile Regression in Reproducing Kernel Hilbert Space
Yi Yang, Teng Zhang, Hui Zou
Expectile, first introduced by Newey and Powell (1987) in the econometrics literature, has recently become increasingly popular in risk management and capital allocation for financ…
Orthogonal Matrix Retrieval in Cryo-Electron Microscopy
Tejal Bhamre, Teng Zhang, Amit Singer
In single particle reconstruction (SPR) from cryo-electron microscopy (cryo-EM), the 3D structure of a molecule needs to be determined from its 2D projection images taken at unknow…
Marchenko-Pastur Law for Tyler's M-estimator
Teng Zhang, Xiuyuan Cheng, Amit Singer
This paper studies the limiting behavior of Tyler's M-estimator for the scatter matrix, in the regime that the number of samples and their dimension both go to infinity, an…
A Majorization-Minimization Algorithm for Computing the Karcher Mean of Positive Definite Matrices
Teng Zhang
An algorithm for computing the Karcher mean of positive definite matrices is proposed, based on the majorization-minimization (MM) principle. The proposed MM algorithm is param…
Multivariate Generalized Gaussian Distribution: Convexity and Graphical Models
Teng Zhang, Ami Wiesel, Maria Sabrina Grec
We consider covariance estimation in the multivariate generalized Gaussian distribution (MGGD) and elliptically symmetric (ES) distribution. The maximum likelihood optimization ass…
Spectral Clustering Based on Local PCA
Ery Arias-Castro, Gilad Lerman, Teng Zhang
We propose a spectral clustering method based on local principal components analysis (PCA). After performing local PCA in selected neighborhoods, the algorithm builds a nearest nei…
Robust computation of linear models by convex relaxation
Gilad Lerman, Michael McCoy, Joel A. Tropp +1
Consider a dataset of vector-valued observations that consists of noisy inliers, which are explained well by a low-dimensional subspace, along with some number of outliers. This wo…
A Novel M-Estimator for Robust PCA
Teng Zhang, Gilad Lerman
We study the basic problem of robust subspace recovery. That is, we assume a data set that some of its points are sampled around a fixed subspace and the rest of them are spread in…
lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers
Gilad Lerman, Teng Zhang
We assume data sampled from a mixture of d-dimensional linear subspaces with spherically symmetric distributions within each subspace and an additional outlier component with spher…
Hybrid Linear Modeling via Local Best-fit Flats
Teng Zhang, Arthur Szlam, Yi Wang +1
We present a simple and fast geometric method for modeling data by a union of affine subspaces. The method begins by forming a collection of local best-fit affine subspaces, i.e.,…
Randomized hybrid linear modeling by local best-fit flats
Teng Zhang, Arthur Szlam, Yi Wang +1
The hybrid linear modeling problem is to identify a set of d-dimensional affine sets in a D-dimensional Euclidean space. It arises, for example, in object tracking and structure fr…
Median K-flats for hybrid linear modeling with many outliers
Teng Zhang, Arthur Szlam, Gilad Lerman
We describe the Median K-Flats (MKF) algorithm, a simple online method for hybrid linear modeling, i.e., for approximating data by a mixture of flats. This algorithm simultaneously…