基于稀疏线性重构的主动学习算法

摘要

The conventional active learning methods have one of the following defects:needing some labeled data selected randomly, ignoring the detail of the data structure, or requiring the fixed scale of the neighborhood to be set in advance. Therefore, a learning algorithm, active learning based on sparse linear reconstruction ( SLR ) , is proposed based on the sparse representation model and the optimum experimental design method. Firstly, the sparse representation method is utilized to obtain the sparse reconstruction matrix. Then, the selection is realized with constraining the sparse reconstructive relationship among each data point and optimizing the reconstruction performance. Theory analysis and simulation results demonstrate that the proposed method selects the appropriate data points without any related prior information and does not need the fixed range between the nearby fields. Meanwhile, compared with the traditional methods such as neighborhood entropy, transductive experimental design and locally linear reconstruction, the proposed algorithm has better performance.%传统的主动学习算法，或需要随机选择已标注样本为基础，或忽略数据的结构细节，或需要预先设定固定的邻域规模。基于稀疏表示模型和最优实验设计方法，文中提出一种基于稀疏线性重构的主动学习算法。该算法首先用稀疏表示模型获得样本和其它样本之间的稀疏重构模式，接着在保证样本间稀疏重构关系和重构样本精度的目标下选择合适的样本。实验结果表明，基于文中算法挑选样本无需任何先验知识，克服其它方法需固定邻域范围的缺点，样本选择结果与近邻熵方法、转换实验设计、局部线性重构方法相比，可获得更好的分类性能。