Marine benthic organisms inhabit a heterogeneous environment in which connectivity between populations occurs mainly through dispersive larval stages, while local selective pressures acting on early life history stages lead to non-random mortality, shaping adaptive genetic structure. In order to test the influence of local adaptation and neutral processes in a marine benthic species with low di...

This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust z-score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maxim...

We introduce a novel framework for image-based 3D reconstruction of urban buildings based on symmetry priors. Starting from image-level edges, we generate a sparse and approximate set of consistent 3D lines. These lines are then used to simultaneously detect symmetric line arrangements while refining the estimated 3D model. Operating both on 2D image data and intermediate 3D feature representat...

A mixture of Student t-distributions (MoT) has been widely used to model multivariate data sets with atypical observations, or outliers for robust clustering. In this paper, we developed a novel robust clustering approach bymodeling the data sets usingmixture of Pearson type VII distributions (MoP). An EM algorithm is developed for the maximum likelihood estimation of the model parameters. An o...

Outlier detection is a task that finds objects that are considerably dissimilar, exceptional or inconsistent with respect to the remaining data. Outlier detection has wide applications which include data analysis, financial fraud detection, network intrusion detection and clinical diagnosis of diseases. In data analysis applications, outliers are often considered as error or noise and are remov...

We investigate the use of biased sampling according to the density of the data set to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multidimensional data sets. In density-biased sampling, the probability that a given point will be included in the sample depends on the local density of the data set. We propose a general technique for densi...

We investigate the use of biased sampling according to the density of the dataset, to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multidimensional datasets. In densitybiased sampling, the probability that a given point will be included in the sample depends on the local density of the dataset. We propose a general technique for density-...

Outlier detection is an important task for the statistical analysis of multivariate data, because often the outliers contain important information about the data structure. In compositional data, represented usually as proportions subject to a unit sum constraint, the ratios between the parts (variables) contain the essential information. This inherent property is, however, incompatible with th...

Mixture of Student t-distribution (MoT) has been widely used to model multivariate data sets with atypical observations, or outliers for robust clustering. In this paper, we developed a novel robust clustering approach by modeling the data sets with mixture of Pearson type VII distribution (MoP). An EM algorithm is developed for the maximum likelihood estimation of the model parameters. Outlier...

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very t...