Analysis of Sparse Subspace Clustering: Experiments and Random Projection

TL;DR

This paper analyzes Sparse Subspace Clustering, demonstrating its effectiveness through experiments and introducing a new approach to reduce its computational time.

Contribution

The paper provides an experimental analysis of Sparse Subspace Clustering and proposes a novel method to improve its computational efficiency.

Findings

Sparse Subspace Clustering is effective for various clustering tasks.

The new approach significantly reduces computational time.

Experimental results validate the improved efficiency.

Abstract

Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner. As a technique that is used in many domains, such as face clustering, plant categorization, image segmentation, document classification, clustering is considered one of the most important unsupervised learning problems. Scientists have surveyed this problem for years and developed different techniques that can solve it, such as k-means clustering. We analyze one of these techniques: a powerful clustering algorithm called Sparse Subspace Clustering. We demonstrate several experiments using this method and then introduce a new approach that can reduce the computational time required to perform sparse subspace clustering.