Robust subspace clustering Page 1 The Annals of Statistics 2014, Vol. 42, No. 2, 669–699
DOI: 10.1214/13-AOS1199 © Institute of Mathematical Statistics, 2014 ROBUST …

Subspace clustering is a powerful technology for clustering data according to the underlying
subspaces. Representation based methods are the most popular subspace clustering …

Recent progress in machine learning (ML), together with advanced computational power,
have provided new research opportunities in cardiovascular modeling. While classifying …

Subspace clustering refers to the problem of grouping data points that lie in a union of low-
dimensional subspaces. One successful approach for solving this problem is sparse …

Many prediction tasks contain uncertainty. In some cases, uncertainty is inherent in the task
itself. In future prediction, for example, many distinct outcomes are equally valid. In other …

An efficient and flexible dictionary structure is proposed for sparse and redundant signal
representation. The proposed sparse dictionary is based on a sparsity model of the …

Scientists and engineers working with large volumes of high-dimensional data often face the
problem of dimensionality reduction: finding meaningful low-dimensional structures hidden …

Over the past few years, several methods for segmenting a scene containing multiple rigidly
moving objects have been proposed. However, most existing methods have been tested on …

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

A phishing attack is one of the most significant problems faced by online users because of its
enormous effect on the online activities performed. In recent years, phishing attacks continue …