对称局部保持的半监督维数约简算法
发布时间：2018-11-16 19:02
【摘要】：针对自然界较多图像具有对称的特点以及数据分布大多呈一定的流形结构情况,提出了一种对称局部保持的半监督维数约减(SLPSDR)算法.该算法使用矩阵定义维数约减映射矩阵元素之间的关系,使图像中对称的像素点对应的映射矩阵的值之间的差别最小;同时为了利用无标签训练样本保持数据的流形结构,要求低维空间中每个点的邻域关系与高维空间中的邻域关系相似.在CMU PIE、Extend Yale B、ORL、AR人脸数据库上的实验结果表明,图像数据明显的对称特点使得SLPSDR算法优于其他对比的维数约减算法.
[Abstract]:In view of the symmetry of many images in nature and the fact that most of the data distribution is manifold structure, a semi-supervised dimension reduction (SLPSDR) algorithm with symmetric local preservation is proposed. In this algorithm, the matrix is used to define the relationship between the elements of the dimensionality reduction mapping matrix, and the difference between the values of the mapping matrix corresponding to the symmetric pixel points in the image is minimized. At the same time, in order to use unlabeled training samples to maintain the manifold structure of data, it is required that the neighborhood relationship of each point in low-dimensional space is similar to that in high-dimensional space. The experimental results on CMU PIE,Extend Yale ORL AR face database show that the obvious symmetry of the image data makes the SLPSDR algorithm superior to other contrast dimension reduction algorithms.
【作者单位】： 广东司法警官职业学院信息管理系;
【基金】：国家自然科学基金资助项目(61402118)~~
【分类号】：TP391.41
本文编号：2336374
[Abstract]:In view of the symmetry of many images in nature and the fact that most of the data distribution is manifold structure, a semi-supervised dimension reduction (SLPSDR) algorithm with symmetric local preservation is proposed. In this algorithm, the matrix is used to define the relationship between the elements of the dimensionality reduction mapping matrix, and the difference between the values of the mapping matrix corresponding to the symmetric pixel points in the image is minimized. At the same time, in order to use unlabeled training samples to maintain the manifold structure of data, it is required that the neighborhood relationship of each point in low-dimensional space is similar to that in high-dimensional space. The experimental results on CMU PIE,Extend Yale ORL AR face database show that the obvious symmetry of the image data makes the SLPSDR algorithm superior to other contrast dimension reduction algorithms.
【作者单位】： 广东司法警官职业学院信息管理系;
【基金】：国家自然科学基金资助项目(61402118)~~
【分类号】：TP391.41
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