In many high-dimensional big data clustering methods,subspace learning is a commonly used technique.Traditional subspace-based methods project high-dimensional data into low-dimensional space to perform dimensionality...In many high-dimensional big data clustering methods,subspace learning is a commonly used technique.Traditional subspace-based methods project high-dimensional data into low-dimensional space to perform dimensionality reduction and clustering.Dimensionality reduction can reduce computational complexity,but it also leads to the loss of some key features.To deal with this issue,we propose a novel clustering algorithm based on a hyperdisk representation that provides a tighter approximation of sample regions.Specifically,the hyperdisk is defined as the intersection between the affine packet and a hypersphere,forming a disk-like region that offers a more compact representation of the class boundaries.This model achieves a balance between the loose approximation of the affine packet and the strict constraints of the convex hull,thereby enhancing the stability and reliability of the algorithm in low-sample high-dimensional classification scenarios compared to traditional hyperellipse models.For optimization,a standard quadratic programming algorithm is utilized to solve the proposed formulation.The performance of the algorithm is comprehensively evaluated from multiple perspectives,and its effectiveness is demonstrated through extensive experimental results.展开更多
几何模型分类器具有坚实的几何统计基础和良好的泛化能力,因此在旋转机械故障诊断中取得了较高的分类精度。与仿射包和凸包相比,超圆盘(Hyperdisk,HD)对样本分布区域的估计更加合理。但超圆盘模型属于浅层学习模型,对复杂函数的表示能...几何模型分类器具有坚实的几何统计基础和良好的泛化能力,因此在旋转机械故障诊断中取得了较高的分类精度。与仿射包和凸包相比,超圆盘(Hyperdisk,HD)对样本分布区域的估计更加合理。但超圆盘模型属于浅层学习模型,对复杂函数的表示能力有限,存在学习能力和泛化能力差等缺点。针对这个问题提出一种深度超圆盘分类器(Deep Hyperdisk Large Margin Classifier,DHD),该方法通过模块叠加的方式将超圆盘分类器深度化,利用特征提取公式从每层模块的输入样本中自主提取新的特征值,并将其应用在下一层模块的训练学习中。将所提方法应用到旋转机械故障诊断当中,实验结果表明该方法对故障样本的分类准确率高于其他模型算法,且对不均衡样本和强噪声背景下的故障样本均具有良好的分类能力。展开更多
基金supported by the National Natural Science Foundation of China(No.62006056)the Guangdong Basic and Applied Basic Research Foundation(Nos.2024A1515012040 and 2023B1515120020)the Science and Technology Planning Project of Guangzhou(No.2024A03J0401).
文摘In many high-dimensional big data clustering methods,subspace learning is a commonly used technique.Traditional subspace-based methods project high-dimensional data into low-dimensional space to perform dimensionality reduction and clustering.Dimensionality reduction can reduce computational complexity,but it also leads to the loss of some key features.To deal with this issue,we propose a novel clustering algorithm based on a hyperdisk representation that provides a tighter approximation of sample regions.Specifically,the hyperdisk is defined as the intersection between the affine packet and a hypersphere,forming a disk-like region that offers a more compact representation of the class boundaries.This model achieves a balance between the loose approximation of the affine packet and the strict constraints of the convex hull,thereby enhancing the stability and reliability of the algorithm in low-sample high-dimensional classification scenarios compared to traditional hyperellipse models.For optimization,a standard quadratic programming algorithm is utilized to solve the proposed formulation.The performance of the algorithm is comprehensively evaluated from multiple perspectives,and its effectiveness is demonstrated through extensive experimental results.
文摘几何模型分类器具有坚实的几何统计基础和良好的泛化能力,因此在旋转机械故障诊断中取得了较高的分类精度。与仿射包和凸包相比,超圆盘(Hyperdisk,HD)对样本分布区域的估计更加合理。但超圆盘模型属于浅层学习模型,对复杂函数的表示能力有限,存在学习能力和泛化能力差等缺点。针对这个问题提出一种深度超圆盘分类器(Deep Hyperdisk Large Margin Classifier,DHD),该方法通过模块叠加的方式将超圆盘分类器深度化,利用特征提取公式从每层模块的输入样本中自主提取新的特征值,并将其应用在下一层模块的训练学习中。将所提方法应用到旋转机械故障诊断当中,实验结果表明该方法对故障样本的分类准确率高于其他模型算法,且对不均衡样本和强噪声背景下的故障样本均具有良好的分类能力。
