摘要
为了提取样本中的非线性模式,保持其中的流形结构以及减少投影时间,该文提出了一种快速核有监督局部保留投影算法。该算法使用有监督聚类选择法选取训练集的一个子集进行子集核主成分分析,然后在子集核主成分分析形成的子空间中进行有监督局部保留投影。实验结果表明:相对于有监督局部保留投影算法以及现有的几种流行特征提取方法,新算法能够取得更高的识别率;相对于现有的核投影算法,新算法的投影速度更快。在有些数据集上,只要普通核投影十分之一左右的时间,就能达到相同甚至更高的识别率。
To extract nonlinear patterns,preserve the manifold structure,and reduce the projection time,a Fast Kernel Supervised Locality Preserving Projection（FKSLPP） algorithm is proposed.This new algorithm firstly selects a subset of the training set by supervised cluster selection algorithm to do Subset Kernel Principal Component Analysis（SKPCA）,and then Supervised Locality Preserving Projection（SLPP） is performed in SKPCA subspace.Experiments results show that compared with SLPP and some other popular feature extraction algorithms,FKSLPP can get higher recognition rates;compared with kernel projection algorithms of state of art,FKSLPP is much faster.In some datasets,FKSLPP can get same or higher recognition rates while costs only one-tenth processing time of the common kernel projection algorithms.
出处
《电子与信息学报》
EI
CSCD
北大核心
2011年第5期1049-1054,共6页
Journal of Electronics & Information Technology
关键词
模式识别
特征提取
有监督局部保留投影
子集核主成分分析
Pattern recognition
Feature extraction
Supervised locality preserving projection
Subset kernel principal component analysis
