The feature distribution of high dimension, small sample size (HDSS) data is sparse, resulting in unsatisfactory clustering results. Dimension reduction methods play an inevitable role in analyzing and visualizing high-dimensional data. It is likely to cause the matrix singularity for subspace clustering when directly reduce the dimension of HDSS dataset. Therefore, we construct multiple data subsets from the original HDSS dataset for ensemble dimension reduction. Projection least square regression subspace clustering (PLSR) which combines projection technique with least-square regression is used as a base dimension reducer for ensemble dimension reduction, called EPLSR. Considering the spectral properties of spectral clustering, we propose the ensemble dimension reduction for subspace clustering based on spectral disturbance (SD-EPLSR) method. According to the theory of spectral disturbance, the weight coefficients are learned according to two principles: 1. The clustering results on each data subset should be close to the consensus clustering result. 2. Data subsets with similar clustering results should have approximate weights. Experiments on eight HDSS datasets demonstrate that our method is effective.

高维小样本（HDSS）数据特征分布稀疏，导致聚类结果不理想。降维方法在分析和可视化高维数据中起着不可避免的作用。直接对HDSS数据集降维容易造成子空间聚类的矩阵奇异。因此，我们从原始 HDSS 数据集构建多个数据子集以进行集成降维。将投影技术与最小二乘回归相结合的投影最小二乘回归子空间聚类 (PLSR) 被用作集成降维的基本降维器，称为 EPLSR。考虑到谱聚类的谱特性，我们提出了基于谱扰动的子空间聚类的集成降维（SD-EPLSR）方法。根据谱扰动理论，权重系数的学习遵循两个原则： 1. 每个数据子集上的聚类结果应该接近一致的聚类结果。2. 聚类结果相似的数据子集应具有近似的权重。在八个 HDSS 数据集上的实验表明我们的方法是有效的。