An Alternative Algorithm for Classification Based on Robust Mahalanobis Distance

Normalized Clustering Algorithm Based on Mahalanobis Distance

FCM (fuzzy c-means algorithm) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. The added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Clustering Algorithm Based on Mahalanobis distance by t...

Mahalanobis distance based on fuzzy clustering algorithm for image segmentation

Anisotropic Superpixel Generation Based on Mahalanobis Distance

Superpixels have been widely used as a preprocessing step in various computer vision tasks. Spatial compactness and color homogeneity are the two key factors determining the quality of the superpixel representation. In this paper, these two objectives are considered separately and anisotropic superpixels are generated to better adapt to local image content. We develop a unimodular Gaussian gene...

Classification with Kernel Mahalanobis Distance Classifiers

Within the framework of kernel methods, linear data methods have almost completely been extended to their nonlinear counterparts. In this paper, we focus on nonlinear kernel techniques based on the Mahalanobis distance. Two approaches are distinguished here. The first one assumes an invertible covariance operator, while the second one uses a regularized covariance. We discuss conceptual and exp...

On Robust Mahalanobis Distance Issued from Fast Mcd and Mvv

In modern activities such as banking, homeland security, information transportation, telecommunication, etc., people work with large and high dimension data sets. But, the higher the dimension the higher the probability that outliers will be present in the data sets. The ability to detect outliers in high dimension multivariate data sets is a challenging task. In this circumstance, robust estim...