基于成对约束和稀疏保留的数据降维算法

结合以成对约束形式给出的监督信息和无监督信息，提出一种基于成对约束和稀疏保留的数据降维算法。通过成对约束信息进行鉴别分析，利用稀疏表示方法保留数据集在变换空间中的全局稀疏结构。实验结果表明，与传统特征抽取算法相比，该算法的识别效果更好，需要调节的参数更少，且鲁棒性较高。

Dimensionality Reduction Algorithm Based on Pair-wise Constraints and Sparsity Preserving

This paper presents a dimensionality reduction algorithm based on pair-wise constraints and sparsity preserving. It combines some supervised information in the form of pair-wise constraints and large number of unsupervised information. It uses pair-wise constraints to discriminant analysis and uses sparse representation to preserve the sparse reconstructive structure in the transformed space. Compared with the traditional feature extraction method, this algorithm has a better recognition impact, lower parameters, and better robustness.