Mahalanobis distance

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

New Multi Access Selection Method Based on Mahalanobis Distance

Abstract: In next generation wireless communications, the evolution of the mobile terminal towards a multimode architecture, will allow the mobile users to benet simultaneously from various radio access technologies (RAT’s). The most important issue is how to choose the most appropriate time to start a redirection of trac ow, and how to choose the most suitable network in terms of quality of service (QoS) for mobile’s user. This paper proposes a novel method based on mahalanobis distance which takes into consideration the correlation with dierent criteria and also aims to choose the optimal network while ensuring no ranking abnormality and reducing the number of handos. Simulation results are

On Improving the Accuracy and Performance of Content-Based File Type Identification

Abstract: Types of files (text, executables, Jpeg images, etc.) can be identified through file extension, magic number, or other header information in the file. However, they are easy to be tampered or corrupted so cannot be trusted as secure ways to identify file types.In the presence of adversaries, analyzing the file content may be a more reliable way to identify file types, but existing approaches of file type analysis still need to be improved in terms of accuracy and speed. Most of them use byte-frequency distribution as a feature in building a representative model of a file type, and apply a distance metric to compare the model with byte-frequency distribution of the file in question. Mahalanobis distance is the most popular distance metric. In this paper, we propose 1) the cosine similarity as a better metric than Mahalanobis distance in terms of classification accuracy, smaller model size, and faster detection rate, and 2) a new type-identification scheme that applies recursive steps to identify types of files. We compare the cosine similarity to Mahalanobis distance using Wei-Hen Li et al.'s single and multi-centroid modeling techniques, which showed 4.8% and 13.10% improvement in classification accuracy (single and multi-centroid respectively). The cosine similarity showed reduction of the model size by about 90% and improvement in the detection speed by 11%. Our proposed type identification scheme showed 37.78% and 31.47% improvement over Wei-Hen Li's single and multi-centroid modeling techniques respectively.

A New Fuzzy Possibility Clustering Algorithms Based on Unsupervised Mahalanobis Distances

Abstract: The well known Fuzzy Possibility C-Mean algorithm could improve the problems of outlier and noise in fuzzy c-mean, but it was based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm, were developed to detect non-spherical structural clusters, but both of them based on semi-supervised Mahalanobis distance, these two algorithms fail to consider the relationships between cluster centers in the objective function, needing additional prior information. The second problem is as follows, when some training cluster size is small than its dimensionality, it induces the singular problem of the inverse covariance matrix. The third important problem is how to select the better initial value to improve the cluster accuracy. In this paper, focusing attention to above three problems, First we added a regulating factor of covariance matrix, -In 1+Sigma-1 i , to each class in objective function, second, a method to reduce the dimensions was proposed .finally, we proposed two methods to select the better initial value, and then, the improved new algorithm, "Fuzzy Possibility C-Mean based on Mahalanobis distance (FPCM-M)", is obtained. A real data set was applied to prove that the performance of the FPCM-M algorithm is better than the traditional FCM, PCM, and FPCM, and the Ratio method and Inverse method which is proposed by us is better than the Random method for selecting the initial values.