In order to solve the problem that high-dimensional input data may have singular value,as well as to improve the operation efficiency and robustness of the algorithm,we propose a new algorithm named block two dimensional locality preserving projections based on L1-norm (B2DLPP-L1).Traditional locality preserving projection (LPP) uses the principal component analysis (PCA) to project input data to PCA subspace to avoid singular value problem,however,input data can lose some effective information in this way.The B2DLPP-L1 algorithm chooses two dimensional data as input data,and it divides original input images into modular images and use the images which are divided into two types as the new input data afterwards.Then we apply the proposed algorithm to the sub-images to reduce the dimensionality.In theory,the B2DLPP-L1 algorithm can better reduce dimensionality,preserve effective information of input data,reduce computation,improve operation efficiency of the algorithm,and overcome the problem of low classification accuracy and improve algorithm robustness.Experimental results on face databases reveal that the B2DLPP-L1 algorithm utilizes less time to accomplish the nearestneighbor classification and obtain more accurate classification rate.%针对高维输入数据维数较大时可能存在奇异值问题,同时为提高算法的运算效率以及算法的鲁棒性,提出了一种基于L1范数的分块二维局部保持投影算法B2DLPP-L1.传统的局部保持投影算法为避免出现奇异值问题,首先运用主成分分析算法将高维数据投影到子空间中,然而这种方式将会造成高维数据中部分有效信息的流失,B2DLPP-L1算法选择将二维数据直接作为输入数据,避免运用向量形式的输入数据时可能造成的数据流失;同时该算法对二维输入数据进行分块处理,将分块后的数据块作为新的输入数据,之后运用基于L1范数的二维局部保持投影算法对其进行降维.理论上,B2DLPP-L1算法能够较好地对数据进行降维,不仅能够保持高维数据中的有效信息,降低计算复杂程度,提高算法的运行效率,同时还能够克服存在外点情况下分类准确率较低问题,提高算法的鲁棒性.通过选择不同的人脸数据库进行实验,实验结果表明,在存在外点的情况下,运用最近邻分类器时能够取得更高的分类准确率,同时所需的分类时间有所减少.
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