sklearn中也有现成的包scipy.spatial.distance.mahalanobis(u,v,VI) 采用马氏距离需要注意的是,数据集的长度(sample个数)必须大于维度,否则无法计算协方差矩阵。
在实际应用中，可以利用Python库scikit-learn中的`mahalanobis`函数来计算马氏距离。具体实现时需注意，数据集的样本数量必须大于维度，否则无法计算协方差矩阵。
Mahalanobis square distance62F3562H10Mahalanobis square distances (MSDs) based on robust estimators improves outlier detection performance in multivariate data. However, the unbiasedness of robust estimators are not guaranteed when the sample size is small and this reduces their performance in outlier ...
Mahalanobis Distance is an effective distance metric that finds the distance between a point and a distribution. It’s very effective on multivariate data.
Outlier detection using statistics provides a simple framework for building a distribution model and for detection based on the variance of the data point from the mean. From: Data Science (Second Edition), 2019 About this pageSet alert Also in subject areas: Computer Science EngineeringDiscover ot...
There are many outlier detection techniques, some are statistical, proximity based, and clustering based methods (Han, Kamber, & Pei, 2012). In fact, for small datasets, outliers can be detected visually as shown in Fig. 2.2, where the data has been visualized using principal component analysi...
Outlier Detection for Compositional Data Using Robust Methods Outlier detection based on the Mahalanobis distance (MD) requires an appropriate transformation in case of compositional data. For the family of logratio t... Peter,FilzmoserKarel,Hron - 《Mathematical Geosciences》 被引量: 173发表: 2008...
Outlier detection is an important problem in statistics. In this paper, we introduce a novel concept of outlier probability for outlier detection and robust linear regression. First, the Mahalanobis distance is utilized to identify the leverage points. By excluding the leverage points, the maximum tr...
A critical problem for several real world applications is class imbalance. Indeed, in contexts like fraud detection or medical diagnostics, standard machin
Robust kalman filtering based on mahalanobis distance as outlier judging criterion [J]. Journal of Geodesy, 2014, 88(4): 391-401.G. Chang, Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion, Journal of Geodesy, 88 (2014) 391-401....