Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method...Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method using Structured Self-Representation( SSR) by simultaneously taking into account the selfrepresentation property and local geometrical structure of features. Concretely,according to the inherent selfrepresentation property of features,the most representative features can be selected. Mean while,to obtain more accurate results,we explore local geometrical structure to constrain the representation coefficients to be close to each other if the features are close to each other. Furthermore,an efficient algorithm is presented for optimizing the objective function. Finally,experiments on the synthetic dataset and six benchmark real-world datasets,including biomedical data,letter recognition digit data and face image data,demonstrate the encouraging performance of the proposed algorithm compared with state-of-the-art algorithms.展开更多
大数据时代背景下,随着所获数据数量和维度的不断增加,高维数据的处理成为聚类分析的重点和难点.基于同一类别高维数据通常分布在高维环绕空间的低维子空间这一事实,子空间聚类成为高维数据聚类分析领域的重要方法.稀疏子空间聚类(Spars...大数据时代背景下,随着所获数据数量和维度的不断增加,高维数据的处理成为聚类分析的重点和难点.基于同一类别高维数据通常分布在高维环绕空间的低维子空间这一事实,子空间聚类成为高维数据聚类分析领域的重要方法.稀疏子空间聚类(Sparse Space Clustering,SSC)通过交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)对数据矩阵的稀疏自表达系数进行求解,发现分布于低维子空间并集中的数据的稀疏表示并进行聚类.但是ADMM参数多、收敛速度慢,其效率难以满足对大规模数据库进行聚类分析的要求.针对这一问题提出了基于L_0约束的稀疏子空间聚类方法,该方法使用正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法求解L_0约束的自表达稀疏重建问题,构建数据集中各数据之间的相关性矩阵,最终对相关性矩阵应用谱聚类方法得到聚类结果.根据OMP算法每次迭代之间的耦合关系对其进行优化,进一步降低了计算复杂度,提高了算法效率.在生成数据和Extended Yale B database人脸数据库的实验结果表明,该算法与SSC相比,在显著减少计算时间的基础上,取得了与SSC相当的聚类准确率.展开更多
分布式聚类挖掘技术是解决数据集分布环境下聚类挖掘问题的有效方法.针对数据水平分布情况,在已有分布式密度聚类算法DBDC(density based distributed clustering)的基础上,引入局部密度聚类和密度吸引子等概念,提出一种基于局部密度的...分布式聚类挖掘技术是解决数据集分布环境下聚类挖掘问题的有效方法.针对数据水平分布情况,在已有分布式密度聚类算法DBDC(density based distributed clustering)的基础上,引入局部密度聚类和密度吸引子等概念,提出一种基于局部密度的分布式聚类算法——LDBDC(local density based distributed clustering).算法适用于含噪声数据和数据分布异常情况,对高雏数据有着良好的适应性.理论分析和实验结果表明,LDBDC算法在聚类质量和算法效率方面优于已有的DBDC算法和SDBDC(scalable dellsity-based distributed clustering)算法.算法是有效、可行的.展开更多
基金Sponsored by the Major Program of National Natural Science Foundation of China(Grant No.13&ZD162)the Applied Basic Research Programs of China National Textile and Apparel Council(Grant No.J201509)
文摘Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method using Structured Self-Representation( SSR) by simultaneously taking into account the selfrepresentation property and local geometrical structure of features. Concretely,according to the inherent selfrepresentation property of features,the most representative features can be selected. Mean while,to obtain more accurate results,we explore local geometrical structure to constrain the representation coefficients to be close to each other if the features are close to each other. Furthermore,an efficient algorithm is presented for optimizing the objective function. Finally,experiments on the synthetic dataset and six benchmark real-world datasets,including biomedical data,letter recognition digit data and face image data,demonstrate the encouraging performance of the proposed algorithm compared with state-of-the-art algorithms.
文摘大数据时代背景下,随着所获数据数量和维度的不断增加,高维数据的处理成为聚类分析的重点和难点.基于同一类别高维数据通常分布在高维环绕空间的低维子空间这一事实,子空间聚类成为高维数据聚类分析领域的重要方法.稀疏子空间聚类(Sparse Space Clustering,SSC)通过交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)对数据矩阵的稀疏自表达系数进行求解,发现分布于低维子空间并集中的数据的稀疏表示并进行聚类.但是ADMM参数多、收敛速度慢,其效率难以满足对大规模数据库进行聚类分析的要求.针对这一问题提出了基于L_0约束的稀疏子空间聚类方法,该方法使用正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法求解L_0约束的自表达稀疏重建问题,构建数据集中各数据之间的相关性矩阵,最终对相关性矩阵应用谱聚类方法得到聚类结果.根据OMP算法每次迭代之间的耦合关系对其进行优化,进一步降低了计算复杂度,提高了算法效率.在生成数据和Extended Yale B database人脸数据库的实验结果表明,该算法与SSC相比,在显著减少计算时间的基础上,取得了与SSC相当的聚类准确率.
文摘分布式聚类挖掘技术是解决数据集分布环境下聚类挖掘问题的有效方法.针对数据水平分布情况,在已有分布式密度聚类算法DBDC(density based distributed clustering)的基础上,引入局部密度聚类和密度吸引子等概念,提出一种基于局部密度的分布式聚类算法——LDBDC(local density based distributed clustering).算法适用于含噪声数据和数据分布异常情况,对高雏数据有着良好的适应性.理论分析和实验结果表明,LDBDC算法在聚类质量和算法效率方面优于已有的DBDC算法和SDBDC(scalable dellsity-based distributed clustering)算法.算法是有效、可行的.
