80 citations · 138 across the 20 of their papers we have counts for
11 papers · 1 filter
Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms
Zhihui Zhu, Yifan Wang, Daniel P. Robinson +3
Recent methods for learning a linear subspace from data corrupted by outliers are based on convex and nuclear norm optimization and require the dimension of the subspace a…
Nonconvex Robust Low-rank Matrix Recovery
Xiao Li, Zhihui Zhu, Anthony Man-Cho So +1
In this paper we study the problem of recovering a low-rank matrix from a number of random linear measurements that are corrupted by outliers taking arbitrary values. We consider a…
On Geometric Analysis of Affine Sparse Subspace Clustering
Chun-Guang Li, Chong You, René Vidal
Sparse subspace clustering (SSC) is a state-of-the-art method for segmenting a set of data points drawn from a union of subspaces into their respective subspaces. It is now well un…
Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks
Siddharth Mahendran, Ming Yang Lu, Haider Ali +1
We consider the task of estimating the 3D orientation of an object of known category given an image of the object and a bounding box around it. Recently, CNN-based regression and c…
Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI
Evan Schwab, Benjamin D. Haeffele, René Vidal +1
Sparse dictionary learning is a popular method for representing signals as linear combinations of a few elements from a dictionary that is learned from the data. In the classical s…
On the Implicit Bias of Dropout
Poorya Mianjy, Raman Arora, Rene Vidal
Algorithmic approaches endow deep learning systems with implicit bias that helps them generalize even in over-parametrized settings. In this paper, we focus on understanding such a…