Abstract: Unsupervised feature extraction has garnered increasing attention for alleviating the “curse of dimensionality” problem posed by high-dimensional data. However, existing methods typically construct low-rank graphs or nearest neighbor graphs to find the projection direction of high-dimensional data, overlooking the global structural correlation and sparsity of representation. To address these issues, a novel dimensionality reduction method called structured sparse graph learning-based unsupervised feature extraction (SSGL) is proposed. The SSGL method utilizes representation to construct nearest neighbor graphs between samples to preserve the local structure of the data and uses least squares regression to model the global structural correlation of the data. Consequently, the proposed SSGL can simultaneously preserve both the local and global structural correlations of the data. Moreover, SSGL employs sparse regularization to disconnect links between samples from different clusters in the affinity graph, thereby making the learned projection more discriminative. To validate the effectiveness of SSGL, extensive experiments are conducted on eight public image datasets. The results indicate that SSGL outperforms other advanced feature extraction methods in terms of clustering accuracy, significantly enhancing clustering results and classification performance.

Key words: feature extraction, sparse graph, affinity relationship, local structure

摘要： 无监督特征提取因解决高维数据造成的“维度灾难”问题而受到越来越多的关注。然而，现有方法通常构建低秩图或者近邻图来寻找高维数据的投影方向，忽略了数据的全局相关结构和表征的稀疏性。为了解决这些问题，提出了一种新的降维方法，被称为面向无监督特征提取的结构化稀疏图学习（SSGL）。SSGL方法使用表征来构建样本之间的最近邻图来保持数据的局部结构，使用最小二乘回归来建模数据的全局相关结构。因此，SSGL能够同时保持数据的局部和全局相关结构。此外，SSGL使用稀疏正则化断开亲和图中不同聚类样本之间的连接，从而使得学到的投影更具有判别力。为了验证SSGL的有效性，在八个公共图像数据集上进行了大量实验。结果表明，SSGL在聚类精度方面优于其他先进的特征提取方法，显著提升了聚类效果和分类性能。

关键词: 特征提取, 稀疏图, 亲和关系, 局部结构