Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

The Graphical Assessment of Multivariate Normality Using SPSS

Abstract: Multivariate analyses depend on multivariate normality assumption. Although the analyses are available in SPSS, it is not possible to assess the assumption from the basic package. Statistical assessment of the normality is available in a specialized package, SPSS Amos, in form of Mardia's multivariate kurtosis. However, graphical assessment of the normality by chi-square versus Mahalanobis distance plot is not available in both of the packages. The aim of this article is to present the steps to construct the plot in SPSS in a point-and-click manner as expected by most SPSS users.

Differences of image classification techniques for land use and land cover classification

Abstract: Land use and land cover classification of remotely sensed data is an important research and commonly used in remote sensing application. In this study, the different types of classification techniques were used by using satellite image of some part of Selangor, Malaysia. For this objective, the land use and land cover was classified with Landsat 8 satellite image and ERDAS Imagine software as the image processing packages. From the classification output, the accuracy assessment and kappa statistic were evaluated to get the most accurate classifier. The optimal performance would be identified by validating the classification results with ground truth data. Of classified image, the Maximum Likelihood technique (overall accuracy 82.5%) is the highest and more applicable for satellite image classification compared with Mahalanobis Distance and Minimum Distance. The accurate classification can produce the correct Land Use and Land Cover map that can be used for many varieties purposes.

Factor analysis based anomaly detection

Abstract: We propose a novel anomaly detection algorithm based on factor analysis and Mahalanobis distance. Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It reduces attribute space from a larger number of variables to a smaller number of factors. The Mahalanobis distance is used to determine the "similarity" of a set of values from an "unknown" sample to a set of values measured from a collection of "known" samples. Combined with factor analysis, Mahalanobis distance is extended to examine whether a given vector is an outlier from a model identified by "factors" based on factor analysis. We present a factor analysis-based network anomaly detection algorithm and apply it to DARPA intrusion detection evaluation data. The experimental results show that the proposed algorithm is able to detect network intrusions with relatively low false alarms.

Fast Training of Support Vector Machines by Extracting Boundary Data

Abstract: Support vector machines have gotten wide acceptance for their high generalization ability for real world applications. But the major drawback is slow training for classification problems with a large number of training data. To overcome this problem, in this paper, we discuss extracting boundary data from the training data and train the support vector machine using only these data. Namely, for each training datum we calculate the Mahalanobis distances and extract those data that are misclassified by the Mahalanobis distances or that have small relative differences of the Mahalanobis distances. We demonstrate the effectiveness of the method for the benchmark data sets.