L1 Graph联合转换学习多观测样本分类算法

不同分布多观测样本分类问题中，训练样本和测试样本来自不同的域，针对如何利用转换学习提高不同分布多观测样本分类性能问题，提出L1 Graph联合转换学习的多观测样本分类算法。首先基于转换学习构建一种非负矩阵三因子分解框架，将其中不变信息作为源域到目标域的转换桥梁；其次，基于稀疏表示思路构造L1 Graph，自适应寻找数据近邻，保留样本及特征几何结构；最后，将两个互补目标函数联合到统一优化问题中，然后利用迭代算法解决优化问题，进而估计出测试样本类别。在USPS Binary数字数据库、Three Domain Object Benchmark数据库和ALOI数据库上进行对比实验，实验结果表明该方法的有效性，既提高了识别精度又保证了算法鲁棒性。

Multiple observation sets classification algorithm based on L1 Graph transfer learning unified model

In the classification problem of multiple observation sets with different distributions, the training samples and test samples are from different domains; aiming at how to use transfer learning to improve the classification performance of multiple observation sets with different distributions, a multiple observation sets classification algorithm based on L1 Graph transfer learning is presented. First of all, a framework of non negative matrix tri factorization based on domain adaptive learning is constructed, in which the unchanged information is regarded as the bridge of knowledge transformation from the source domain to the target domain; The second step is to construct L1 Graph on the basis of sparse representation, adaptively search neighbor data and preserve the geometric structure of samples and features; Lastly, two complementary objective functions are integrated into a unified optimization problem, and then an iterative algorithm is adopted to solve the optimization problem, and the category of the test samples is estimated. Three comparative experiments were conducted on USPS Binary handwritten digit dataset, three Domain Object Benchmark dataset and ALOI dataset, the experiment results verify the effectiveness of the proposed algorithm, which improves the recognition accuracy and also ensures the robustness of the algorithm.