Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. Robust PCA (RPCA) refers to the problem of PCA when the data may be …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Online learning with feature spaces that are not fixed but can vary over time renders a
seemingly flexible learning paradigm thus has drawn much attention. Unfortunately, two …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Recording sensor data is seldom a perfect process. Failures in power, communication or
storage can leave occasional blocks of data missing, affecting not only real-time monitoring …

Background Principal component analysis (PCA) is an essential method for analyzing single-
cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation …

Subspace estimation from unbalanced and incomplete data matrices: l2,infty statistical
guarantees Page 1 The Annals of Statistics 2021, Vol. 49, No. 2, 944–967 https://doi.org/10.1214/20-AOS1986 …

“Principal Component Analysis”(PCA) is an established linear technique for dimensionality
reduction. It performs an orthonormal transformation to replace possibly correlated variables …

Principal component analysis (PCA) and subspace estimation (SE) are popular data
analysis tools and used in a wide range of applications. The main interest in PCA/SE is for …

Network attacks, intrusion detection, and intrusion prevention are important topics in cyber
security. Network flows and system events generate big data, which often leads to …