【摘要】 LSPE（Locality and Similarity Preserving Embedding）特征选择算法首先基于KNN定义图结构来保持数据的局部性,再基于定义图学习数据的低维重构系数来保持数据的局部性和相似性。两个步骤独立进行,缺乏交互。由于近邻个数是人为定义的,使得学习到的图结构不具备自适应的近邻,不是最优的,进而影响算法性能。为优化LSPE算法的性能,提出改进的局部和相似性保持特征选择算法,将图学习与稀疏重构、特征选择并入同一个框架,使得图学习和稀疏编码同时进行,其要求编码过程是稀疏的,自适应近邻的和非负的。所提算法旨在寻找一个能保持数据的局部性和相似性的投影,并对投影矩阵施加l_2,1范数,进而选择能够保持局部性和相似性的相关特征。实验结果表明,改进后的算法减少了主观人为影响,消除了选择特征的不稳定性,对数据噪声鲁棒性更强,提高了图像分类的准确率。更多还原

【Abstract】 LSPE（Locality and similarity preserving embedding） feature selection algorithm firstly maintains the locality of the data based on the pre-defined graph structure of the KNN,and then maintains the locality and similarity of the data based on the low-dimensional reconstruction coefficients that define the learning data of the graph.The two steps are independent and lack of interaction.Since the number of nearest neighbors is artificially defined,the learned graph structure does not have adaptive nearest neighbors and is not optimal,which will affect the performance of the algorithm.In order to optimize the performance of LSPE,an improved locality and similarity preserving feature selection algorithm is proposed.The proposed algorithm incorporates graph learning,sparse reconstruction and feature selection into the same framework,making graph learning and sparse coding are carried out simultaneously.The coding process is required to to be sparse,adaptive neighbor and non-negative.The goal is to find a projection that can maintain the locality and similarity of the data,and apply a l_2,1-norm to the projection matrix,and then select the relevant features that can maintain locality and similarity.Experimental results show that the improved algorithm reduces the subjective influence,eliminates the instability of selecting features,is more robust to data noise,and improves the accuracy of ima-ge classification.更多还原