A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, e...A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.展开更多
There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it de...There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.展开更多
针对机械系统状态监测与故障诊断中存在的故障特征维数较高及模式识别导致的耗时较高问题,提出了一种基于自适应局部保持投影(Locality Preserving Projection,LPP)特征降维和改进多变量预测模型(Variable Predictive Model based Class...针对机械系统状态监测与故障诊断中存在的故障特征维数较高及模式识别导致的耗时较高问题,提出了一种基于自适应局部保持投影(Locality Preserving Projection,LPP)特征降维和改进多变量预测模型(Variable Predictive Model based Class Discriminate,VPMCD)的故障诊断方法。首先,从滚动轴承振动信号中提取时频域特征、能量特征,以及复杂度特征组成高维故障特征数据集;其次,利用自适应LPP方法对高维故障特征数据集进行降维处理,得到低维敏感故障特征;最后,采用改进VPMCD方法对低维敏感故障特征进行分类识别,进而判断故障类型。通过滚动轴承故障诊断试验分析表明,自适应LPP方法克服了传统LPP方法需要人工选取参数的缺陷,在获得低维敏感故障特征的基础上具有较少计算时间,相比主成分分析(Principal Component Analysis,PCA)、局部切空间排列(Local Tangent Space Alignment,LTSA)、线性局部切空间排列(Linear Local Tangent Space Alignment,LLTSA)、等距特征映射(Isometric Mapping,Isomap),以及局部线性嵌入(Locally Linear Embedding,LLE)等算法具有明显的优势;改进VPMCD方法可克服人工选择模型的偶然性和片面性,在滚动轴承10种故障状态的识别中获得了99.4%的诊断精度,相比优化参数支持向量机方法提高了故障诊断效率,大大降低了识别时间,具有一定的优越性。展开更多
针对带钢表面缺陷识别率受到光照变化、纹理复杂多样以及噪声干扰而导致误识别率高的问题,提出一种新的带钢表面缺陷识别算法。首先从增加邻域联系的角度改进多块局部二值模式(MB-LBP)特征,缓解提取过程中因所选子窗口尺寸大小不同而造...针对带钢表面缺陷识别率受到光照变化、纹理复杂多样以及噪声干扰而导致误识别率高的问题,提出一种新的带钢表面缺陷识别算法。首先从增加邻域联系的角度改进多块局部二值模式(MB-LBP)特征,缓解提取过程中因所选子窗口尺寸大小不同而造成的保留图像细节与去除噪声之间的平衡性问题;其次将改进的MB-LBP特征与梯度方向直方图(HOG)特征线性加权得到融合特征,弥补MB-LBP特征没有表征缺陷边缘和方向的缺点,从而更全面地表征复杂的缺陷纹理;最后通过同时增加全局信息和监督信息改善的局部保持投影(LPP)算法将高维的融合特征非线性映射到低维的本质特征空间中,减少融合特征冗余对分类器识别率的影响。在NEU数据集上仿真实验结果表明:算法对光照变化、纹理复杂多样、以及噪声具有一定的鲁棒性,在信噪比为50 d B情况下将带钢表面缺陷识别准确率提高了5. 17%。展开更多
基金Supported by the National Natural Science Foundation of China(60772066)
文摘A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.
文摘There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.
文摘针对机械系统状态监测与故障诊断中存在的故障特征维数较高及模式识别导致的耗时较高问题,提出了一种基于自适应局部保持投影(Locality Preserving Projection,LPP)特征降维和改进多变量预测模型(Variable Predictive Model based Class Discriminate,VPMCD)的故障诊断方法。首先,从滚动轴承振动信号中提取时频域特征、能量特征,以及复杂度特征组成高维故障特征数据集;其次,利用自适应LPP方法对高维故障特征数据集进行降维处理,得到低维敏感故障特征;最后,采用改进VPMCD方法对低维敏感故障特征进行分类识别,进而判断故障类型。通过滚动轴承故障诊断试验分析表明,自适应LPP方法克服了传统LPP方法需要人工选取参数的缺陷,在获得低维敏感故障特征的基础上具有较少计算时间,相比主成分分析(Principal Component Analysis,PCA)、局部切空间排列(Local Tangent Space Alignment,LTSA)、线性局部切空间排列(Linear Local Tangent Space Alignment,LLTSA)、等距特征映射(Isometric Mapping,Isomap),以及局部线性嵌入(Locally Linear Embedding,LLE)等算法具有明显的优势;改进VPMCD方法可克服人工选择模型的偶然性和片面性,在滚动轴承10种故障状态的识别中获得了99.4%的诊断精度,相比优化参数支持向量机方法提高了故障诊断效率,大大降低了识别时间,具有一定的优越性。
文摘针对带钢表面缺陷识别率受到光照变化、纹理复杂多样以及噪声干扰而导致误识别率高的问题,提出一种新的带钢表面缺陷识别算法。首先从增加邻域联系的角度改进多块局部二值模式(MB-LBP)特征,缓解提取过程中因所选子窗口尺寸大小不同而造成的保留图像细节与去除噪声之间的平衡性问题;其次将改进的MB-LBP特征与梯度方向直方图(HOG)特征线性加权得到融合特征,弥补MB-LBP特征没有表征缺陷边缘和方向的缺点,从而更全面地表征复杂的缺陷纹理;最后通过同时增加全局信息和监督信息改善的局部保持投影(LPP)算法将高维的融合特征非线性映射到低维的本质特征空间中,减少融合特征冗余对分类器识别率的影响。在NEU数据集上仿真实验结果表明:算法对光照变化、纹理复杂多样、以及噪声具有一定的鲁棒性,在信噪比为50 d B情况下将带钢表面缺陷识别准确率提高了5. 17%。
