摘要
为有效提取磨粒图像的数字化特征,引入局部保持投影算法。针对局部保持投影在磨粒特征降维中的不足,提出一种基于Parzen窗和成对约束的半监督局部保持投影算法(PSS-LPP)。利用Parzen窗估计高维特征空间中样本的密度,然后根据各样本密度自适应调整邻域参数,并且充分利用样本的标签信息和实例约束生成成对约束集,进而指导投影权矩阵的构造,从而实现特征参数的半监督降维。将PSS-LPP应用于磨粒图像的纹理特征降维,研究结果表明:PSS-LPP对邻域参数初值和热核参数不敏感,降维性能比较稳定,磨粒识别精度明显提高。PSS-LPP可以更有效提取磨粒图像的低维特征。
To effectively extract digital features of wear particle images, the locality preserving projection algorithm was employed. For the disadvantages of locality preserving projection for feature dimensionality reduction of wear particles, a semi-supervised locality preserving projection algorithm（PSS-LPP） based on Parzen windows and pairwise constrains was proposed. Parzen windows were utilized to estimate the density of samples in high-dimensional feature space, and the formation of samples of labels and constrains were employed to create pairwise constrains sets which guided the construction of the projection right matrix. Then, with the projection right matrix, semi-supervised dimensionality reduction of feature parameters was implemented. PSS-LPP was applied for texture feature dimensionality reduction of wear particle images. The results indicate that PSS-LPP is not sensitive to the original value of neighborhood parameter and the kernel parameter, thus it has very stable dimensional-reduction performance. The classification accuracy is improved obviously. PSS-LPP can extract low-dimensional features of wear particle images more effectively.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2015年第8期2937-2943,共7页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(50705097)
清华大学摩擦学国家重点实验室开放基金资助项目(SKLTKF09B06)~~
关键词
磨粒分析
局部保持投影
特征提取
PARZEN窗
成对约束
半监督
wear particle analysis
locality preserving projection
feature extraction
Parzen windows
pairwise constrains
semi-supervised
