Laplacian regularized low rank subspace clustering

TL;DR

This paper introduces a Laplacian regularized low rank subspace clustering model that improves data clustering accuracy by using a clean dictionary and graph regularization, outperforming existing methods.

Contribution

It proposes a novel Laplacian regularized low rank subspace clustering model combining clean data representation and graph regularization for enhanced clustering performance.

Findings

Achieves lower clustering error compared to traditional models.

Outperforms several state-of-the-art subspace clustering methods.

Demonstrates improved robustness to noise and gross errors.

Abstract

The problem of fitting a union of subspaces to a collection of data points drawn from multiple subspaces is considered in this paper. In the traditional low rank representation model, the dictionary used to represent the data points is chosen as the data points themselves and thus the dictionary is corrupted with noise. This problem is solved in the low rank subspace clustering model which decomposes the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and gross errors. Also, the clustering results of the low rank representation model can be enhanced by using a graph of data similarity. This model is called Laplacian regularized low rank representation model with a graph regularization term added to the objective function. Inspired from the above two ideas, in this paper a Laplacian regularized low rank subspace clustering model is proposed. This model uses a clean dictionary to represent the data points and a graph regularization term is also incorporated in the objective function. Experimental results show that, compared with the traditional low rank representation model, low rank subspace clustering model and several other state-of-the-art subspace clustering model, the model proposed in this paper can get better subspace clustering results with lower clustering error.