Subspace clustering refers to clustering data points into their respective subspaces and finding a low-dimensional structure to fit each group of points. In subspace clustering, the inter-cluster correlation of data which is caused by noise such as illumination and background affects the performance of subspace clustering algorithms. To solve this problem, a new approach is proposed to detect the unusual data with strong inter-cluster correlation based on the representation matrix obtained from low-rank representation (LRR). Then a low-rank model was established by reducing the unusual part of data and subspace clustering is performed. In addition, in order to apply subspace clustering algorithm on unaligned data, the preprocessing is required to make the data aligned, and the preprocessed data is used in experiments. Experimental results on the face dataset and texture dataset show the efficiency of the proposed method. The experiment also indicates that with the signal ratio increasing, the inter-cluster correlation is becoming more obvious.

子空间聚类是指将数据点聚类到它们各自的子空间中，并找到适合每个点组的低维结构。在子空间聚类中，由诸如照明和背景之类的噪声引起的数据的集群间相关性影响子空间聚类算法的性能。为了解决这个问题，提出了一种新的方法，该方法基于从低秩表示（LRR）获得的表示矩阵来检测具有强集群间相关性的异常数据。然后，通过减少数据的不正常部分来建立低秩模型，并执行子空间聚类。另外，为了对未对齐的数据应用子空间聚类算法，需要进行预处理以使数据对齐，并将预处理后的数据用于实验中。在面部数据集和纹理数据集上的实验结果表明了该方法的有效性。实验还表明，随着信号比的增加，集群间相关性变得越来越明显。