Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

Subspace clustering refers to the problem of segmenting data drawn from a union of
subspaces. State-of-the-art approaches for solving this problem follow a two-stage …

Abstract Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are both
considered as the state-of-the-art methods for {\em subspace clustering}. The two methods …

Corporations, organizations, and governments have collected, digitized, and stored
information in digital forms since the invention of computers, and the speed of such data …

Subspace clustering is an unsupervised learning problem that aims at grou** data points
into multiple clusters''so that data points in a single cluster lie approximately on a low …

Subspace clustering is an unsupervised clustering technique designed to cluster data that is
supported on a union of linear subspaces, with each subspace defining a cluster with …

We consider the problem of simultaneously clustering and learning a linear representation
of data lying close to a union of low-dimensional manifolds, a fundamental task in machine …