融合全局与局部信息，提升高维聚类的特征选择效果
GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering
- 结合流形学习与自表示正则，挖掘样本的局部几何与全局相关性
- 在真实数据上实现更优的特征选择与聚类精度
- 适合无标签高维数据的特征筛选，尤其适用于生物医学分析
高维聚类中识别判别性特征至关重要。由于缺乏聚类标签，传统监督特征选择的正则化方法无法直接使用。为此，我们提出一种新的无监督特征选择方法——基于全局与局部信息融合的特征选择（GOLFS），用于高维聚类问题。GOLFS算法通过流形学习捕捉局部几何结构，并利用正则化自表示建模样本的全局相关性，联合优化特征选择与聚类。该融合策略提升了特征选择和聚类的准确性。同时，我们设计了迭代求解算法并证明其收敛性。模拟实验及两个真实数据应用表明，GOLFS在有限样本下表现出优异的特征选择与聚类性能。
原文摘要 · Abstract (English)
It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the pseudo labels and select the discriminative features simultaneously, we propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS), for high dimensional clustering problems. The GOLFS algorithm combines both local geometric structure via manifold learning and global correlation structure of samples via regularized self-representation to select the discriminative features. The combination improves the accuracy of both feature selection and clustering by exploiting more comprehensive information. In addition, an iterative algorithm is proposed to solve the optimization problem and the convergency is proved. Simulations and two real data applications demonstrate the excellent finite-sample performance of GOLFS on both feature selection and clustering.
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