Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified...Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria.展开更多
针对传统局部线性嵌入算法在挖掘局部流形结构时未充分考虑样本邻居分布信息,且在降维过程中默认样本具有相同的重要性导致提取鉴别特征不明显的问题,提出基于共享近邻的加权局部线性嵌入(weighted local linear embedding based on sha...针对传统局部线性嵌入算法在挖掘局部流形结构时未充分考虑样本邻居分布信息,且在降维过程中默认样本具有相同的重要性导致提取鉴别特征不明显的问题,提出基于共享近邻的加权局部线性嵌入(weighted local linear embedding based on shared neighbors,SN-WLLE)算法,并用于滚动轴承故障诊断.该算法首先使用余弦距离划分样本邻域;其次计算样本邻域对相似度用以评估样本共享近邻信息,并结合样本的6种邻居分布修正局部结构挖掘,提高多共享近邻的k近邻重构准确性;接着从多流形的角度评估样本点与近邻点间的稀疏分布一致性,以获得样本的重要性指标,并在低维空间保持该信息,进而提取准确的鉴别特征;最后结合KNN分类器构建出完备的轴承故障诊断模型.采用凯斯西储大学轴承数据集和实验室测试平台轴承数据集,从可视化评估、定量聚类评估、故障识别精度评估及鲁棒性评估等方面进行分析.结果表明:SN-WLLE算法的F值保持在108以上水准,平均故障识别精度最低可达0.9734,不仅具有较好的类内紧致性与类间可分性,还对近邻参数k具有低敏感性.展开更多
An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-neares...An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.展开更多
Consider the regression model Y=Xβ+ g(T) + e. Here g is an unknown smoothing function on [0, 1], β is a l-dimensional parameter to be estimated, and e is an unobserved error. When data are randomly censored, the est...Consider the regression model Y=Xβ+ g(T) + e. Here g is an unknown smoothing function on [0, 1], β is a l-dimensional parameter to be estimated, and e is an unobserved error. When data are randomly censored, the estimators βn* and gn*forβ and g are obtained by using class K and the least square methods. It is shown that βn* is asymptotically normal and gn* achieves the convergent rate O(n-1/3).展开更多
在机器学习和模式识别中,降维能够显著提升分类器的判别性能与效率。比率和(ratio sum,RS)是线性判别分析(linear discriminant analysis,LDA)的一种全新变体,它试图使投影矩阵在每个维度上都达到最优。但RS并没有考虑到数据的局部几何...在机器学习和模式识别中,降维能够显著提升分类器的判别性能与效率。比率和(ratio sum,RS)是线性判别分析(linear discriminant analysis,LDA)的一种全新变体,它试图使投影矩阵在每个维度上都达到最优。但RS并没有考虑到数据的局部几何结构,这就可能导致无法求得最优解。为了克服RS的这一缺点,提出了一种自适应近邻局部比值和线性判别分析算法(adaptive neighbor local ratio sum linear discriminant analysis,ANLRSLDA)。该算法使用自适应近邻的构图方法构建邻接矩阵,保留数据的局部几何结构完成了数据类间及类内矩阵的构建,从而更好地找到数据的最优表示;并且该方法采用有效的无核参数邻域分配策略来构造邻接矩阵,避免调整热核参数的需要。在UCI数据集及人脸数据集进行了对比实验,验证了该算法的有效性。展开更多
文摘Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria.
文摘针对传统局部线性嵌入算法在挖掘局部流形结构时未充分考虑样本邻居分布信息,且在降维过程中默认样本具有相同的重要性导致提取鉴别特征不明显的问题,提出基于共享近邻的加权局部线性嵌入(weighted local linear embedding based on shared neighbors,SN-WLLE)算法,并用于滚动轴承故障诊断.该算法首先使用余弦距离划分样本邻域;其次计算样本邻域对相似度用以评估样本共享近邻信息,并结合样本的6种邻居分布修正局部结构挖掘,提高多共享近邻的k近邻重构准确性;接着从多流形的角度评估样本点与近邻点间的稀疏分布一致性,以获得样本的重要性指标,并在低维空间保持该信息,进而提取准确的鉴别特征;最后结合KNN分类器构建出完备的轴承故障诊断模型.采用凯斯西储大学轴承数据集和实验室测试平台轴承数据集,从可视化评估、定量聚类评估、故障识别精度评估及鲁棒性评估等方面进行分析.结果表明:SN-WLLE算法的F值保持在108以上水准,平均故障识别精度最低可达0.9734,不仅具有较好的类内紧致性与类间可分性,还对近邻参数k具有低敏感性.
文摘An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.
文摘Consider the regression model Y=Xβ+ g(T) + e. Here g is an unknown smoothing function on [0, 1], β is a l-dimensional parameter to be estimated, and e is an unobserved error. When data are randomly censored, the estimators βn* and gn*forβ and g are obtained by using class K and the least square methods. It is shown that βn* is asymptotically normal and gn* achieves the convergent rate O(n-1/3).
文摘在机器学习和模式识别中,降维能够显著提升分类器的判别性能与效率。比率和(ratio sum,RS)是线性判别分析(linear discriminant analysis,LDA)的一种全新变体,它试图使投影矩阵在每个维度上都达到最优。但RS并没有考虑到数据的局部几何结构,这就可能导致无法求得最优解。为了克服RS的这一缺点,提出了一种自适应近邻局部比值和线性判别分析算法(adaptive neighbor local ratio sum linear discriminant analysis,ANLRSLDA)。该算法使用自适应近邻的构图方法构建邻接矩阵,保留数据的局部几何结构完成了数据类间及类内矩阵的构建,从而更好地找到数据的最优表示;并且该方法采用有效的无核参数邻域分配策略来构造邻接矩阵,避免调整热核参数的需要。在UCI数据集及人脸数据集进行了对比实验,验证了该算法的有效性。
