Locally linear representation for image clustering

TL;DR

This paper introduces Locally Linear Representation (LLR), a novel method combining pairwise distance and linear representation to improve similarity graph construction for image clustering, addressing noise sensitivity and inter-subspace point selection issues.

Contribution

The paper proposes LLR, a new algorithm that integrates pairwise distance and linear representation for better similarity graph construction in clustering.

Findings

LLR improves clustering accuracy over existing methods.

Experimental results validate LLR's effectiveness in subspace learning.

LLR is robust to noise and outliers.

Abstract

It is a key to construct a similarity graph in graph-oriented subspace learning and clustering. In a similarity graph, each vertex denotes a data point and the edge weight represents the similarity between two points. There are two popular schemes to construct a similarity graph, i.e., pairwise distance based scheme and linear representation based scheme. Most existing works have only involved one of the above schemes and suffered from some limitations. Specifically, pairwise distance based methods are sensitive to the noises and outliers compared with linear representation based methods. On the other hand, there is the possibility that linear representation based algorithms wrongly select inter-subspaces points to represent a point, which will degrade the performance. In this paper, we propose an algorithm, called Locally Linear Representation (LLR), which integrates pairwise distance with linear representation together to address the problems. The proposed algorithm can automatically encode each data point over a set of points that not only could denote the objective point with less residual error, but also are close to the point in Euclidean space. The experimental results show that our approach is promising in subspace learning and subspace clustering.