Towards Robust Subspace Clustering via Joint Feature Extraction and Cauchy Loss Function

Abstract

The purpose of the subspace clustering approach is to discover the similarity between samples by learning a self-representation matrix, and it has been widely employed in machine learning and pattern recognition. Most existing subspace clustering techniques discover subspace structures from raw data and simply adopt L2 loss to characterize the reconstruction error. To break through these limitations, a novel robust model named Feature extraction and Cauchy loss function-based Subspace Clustering (FCSC) is proposed. FCSC performs low dimensional and low-rank feature extraction at the same time, as well as processing large noise in the data to generate a more ideal similarity matrix. Furthermore, we provide an efficient iterative strategy to solve the resultant problem. Extensive experiments on benchmark datasets confirm its superiority in the robustness of some advanced subspace clustering algorithms.