Learning a low-rank matrix from missing and corrupted observations is a fundamental
problem in many machine learning applications. However, the role of side information in low …

Principal component analysis (PCA) and Kernel principal component analysis (KPCA) are
fundamental methods in machine learning for dimensionality reduction. The former is a …

We study lower bounds on adaptive sensing algorithms for recovering low rank matrices
using linear measurements. Given an $ n\times n $ matrix $ A $, a general linear …

What learning algorithms can be run directly on compressively-sensed data? In this work,
we consider the question of accurately and efficiently computing low-rank matrix or tensor …

Estimating covariance matrices is a fundamental technique in various domains, most notably
in machine learning and signal processing. To tackle the challenges of extensive …

Abstract Principal Component Analysis (PCA) is a fundamental method for estimating a
linear subspace approximation to high-dimensional data. Many algorithms exist in literature …

Level-set optimization formulations with data-driven constraints minimize a regularization
functional subject to matching observations to a given error level. These formulations are …

Compressed robust principal component analysis (RPCA), in which a low-rank matrix and a
sparse matrix are recovered from an underdetermined amount of noisy linear measurements …

We propose a novel compressive imager for detecting anomalous spectral profiles in a
scene. We model the background spectrum as a low-dimensional subspace while assuming …

We present a novel unified framework robust structure-aware semi-supervised learning
called Unified RSSL (URSSL) which is robust to both outliers and noisy labels where the …