Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In thi...Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.展开更多
针对非线性系统故障诊断难以解决的问题,提出了一种基于扩展局部线性嵌入映射(Locally Linear Embedding,LLE)的故障诊断方法.通过引入切空间距离代替欧氏距离,可以更加科学的满足算法近邻点局部线性的要求,从而可以更好的保留原始数据...针对非线性系统故障诊断难以解决的问题,提出了一种基于扩展局部线性嵌入映射(Locally Linear Embedding,LLE)的故障诊断方法.通过引入切空间距离代替欧氏距离,可以更加科学的满足算法近邻点局部线性的要求,从而可以更好的保留原始数据的局部流形特征.另外,将故障状态与高维空间分布结合起来,通过确定数据点在空间超球内的分布完成故障的检测,在这个过程中将超球的确定与LLE算法中基于核函数的样本外数据扩展相结合,减少了计算量,提高了算法的实时性,从而为复杂非线性系统的故障诊断提供了一种新的有效的方法.展开更多
局部切空间排列算法(Local Tangent Space Alignment)是一种具有严格数学推理的流形学习算法,能有效地学习出高维数据的低维嵌入坐标,但也存在一些不足,如对近邻点的选取依赖性较强、不适应处理高曲率分布、稀疏分布数据源。针对这些缺...局部切空间排列算法(Local Tangent Space Alignment)是一种具有严格数学推理的流形学习算法,能有效地学习出高维数据的低维嵌入坐标,但也存在一些不足,如对近邻点的选取依赖性较强、不适应处理高曲率分布、稀疏分布数据源。针对这些缺点,提出了一种基于几何距离摄动的局部切空间排列算法。利用几何摄动条件把样本空间划分为一组线性分块的组合,在每一个线性块上应用LTSA算法完成降维。实验结果表明了该算法的有效性。展开更多
基金the National Natural Science Foundation of China(No.61004088)the Key Basic Research Foundation of Shanghai Municipal Science and Technology Commission(No.09JC1408000)
文摘Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.
文摘针对非线性系统故障诊断难以解决的问题,提出了一种基于扩展局部线性嵌入映射(Locally Linear Embedding,LLE)的故障诊断方法.通过引入切空间距离代替欧氏距离,可以更加科学的满足算法近邻点局部线性的要求,从而可以更好的保留原始数据的局部流形特征.另外,将故障状态与高维空间分布结合起来,通过确定数据点在空间超球内的分布完成故障的检测,在这个过程中将超球的确定与LLE算法中基于核函数的样本外数据扩展相结合,减少了计算量,提高了算法的实时性,从而为复杂非线性系统的故障诊断提供了一种新的有效的方法.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.61074018)
文摘局部切空间排列算法(Local Tangent Space Alignment)是一种具有严格数学推理的流形学习算法,能有效地学习出高维数据的低维嵌入坐标,但也存在一些不足,如对近邻点的选取依赖性较强、不适应处理高曲率分布、稀疏分布数据源。针对这些缺点,提出了一种基于几何距离摄动的局部切空间排列算法。利用几何摄动条件把样本空间划分为一组线性分块的组合,在每一个线性块上应用LTSA算法完成降维。实验结果表明了该算法的有效性。
