To study the quality of stellar spectra Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and correctness corresponding parameters derived by LASP (LAMOST Stellar Parameter Pipeline), outlier analysis method is applied to archived AFGK stars in fifth data release (DR5) LAMOST. The factor defined order sort more than 3 million selected from DR5 catalog. We propose an improved Lo...

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...

In modern complex industrial processes, mode changes cause unplanned shutdowns, potentially shortening the lifespan of key equipment and incurring significant maintenance costs. To avoid this problem, a method that can detect fault operating in various modes is required. Therefore, we propose novel detection uses k-nearest neighbor normalization-based weight local outlier factor (WLOF). The pro...

Background: Outlier detection is an important factor in data mining since it is used in various real time applications. Outlier is an extreme points that are not related to any of the class. Dealing with dimensions is the great challenge, due to “curse of dimensionality”, for effective outlier detection. In a high dimensional data space, it is difficult to detect most related points and most un...

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...

This paper describes a methodology for the application of hierarchical clustering methods to the task of outlier detection. The methodology is tested on the problem of cleaning Official Statistics data. The goal is to detect erroneous foreign trade transactions in data collected by the Portuguese Institute of Statistics (INE). These transactions are a minority, but still they have an important ...

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...

In this paper we propose a multivariate discount weighted regression technique to give a tractable solution to the problem of variance estimation and forecasting for the multivariate local level model. We give the correspondence between discount regression and matrix normal dynamic linear models and we show that the local level model can be treated with discount regression techniques. We illust...