摘要
Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.
局部保持投影(LPP)是一种典型的降维方法,通过保持数据的内在几何结构,LPP能够获得潜在的判别能力。然而,传统LPP的性能取决于人工预定义的近邻图,并且严重依赖于最近邻标准在原始数据空间中的性能。因此本文提出了一种新的降维算法——自助型局部保持投影(sdLPP)。该方法首先执行LPP获得投影方向,然后在其变换的空间更新近邻图,并重复LPP。另外,本文还提出了一种改进的拉普拉斯打分(Laplacian score)标准作为算法迭代终止和判别力的参考。最后,在几个公共的UCI和人脸数据集上验证了该方法的有效性。
基金
Supported by the National Natural Science Foundation of China (60973097)
the Scientific Research Foundation of Liaocheng University(X0810029)~~
