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
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
局部线性嵌入(Locally linear embedding,LLE)算法通过局部线性来逼近全局的非线性,优点在于可保持降维前后样本点近邻之间的线性结构不变,并且计算速度较快。但是该算法对近邻值的选择十分敏感,不同近邻点数的选择对降维效果影响较大...局部线性嵌入(Locally linear embedding,LLE)算法通过局部线性来逼近全局的非线性,优点在于可保持降维前后样本点近邻之间的线性结构不变,并且计算速度较快。但是该算法对近邻值的选择十分敏感,不同近邻点数的选择对降维效果影响较大。针对此问题,利用残差作为评价降维前后保持样本距离信息优劣的指标,提出一种改进的可变近邻局部线性嵌入(Variable K-nearest neighbor locally linear embedding,VKLLE)算法,即通过给定一个最大近邻数目值,比较降维前后的残差值,根据较小值选择最优的近邻点数,从而使得每个样本点的近邻点数可据残差值进行调整。通过对手写体数字(Mixingnational institute of standards and technology,MNIST)数据集的仿真分析,并与LLE算法进行比较,此方法降维效果更好,计算复杂度也明显降低。最后将该算法运用于轴承状态识别,取得了较好的效果,同时还有效地提高了分类性能和稳定性。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).
文摘局部线性嵌入(Locally linear embedding,LLE)算法通过局部线性来逼近全局的非线性,优点在于可保持降维前后样本点近邻之间的线性结构不变,并且计算速度较快。但是该算法对近邻值的选择十分敏感,不同近邻点数的选择对降维效果影响较大。针对此问题,利用残差作为评价降维前后保持样本距离信息优劣的指标,提出一种改进的可变近邻局部线性嵌入(Variable K-nearest neighbor locally linear embedding,VKLLE)算法,即通过给定一个最大近邻数目值,比较降维前后的残差值,根据较小值选择最优的近邻点数,从而使得每个样本点的近邻点数可据残差值进行调整。通过对手写体数字(Mixingnational institute of standards and technology,MNIST)数据集的仿真分析,并与LLE算法进行比较,此方法降维效果更好,计算复杂度也明显降低。最后将该算法运用于轴承状态识别,取得了较好的效果,同时还有效地提高了分类性能和稳定性。
