Mahalanobis distance

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

Robust Sensor Correlation Analysis For Machine Condition Monitoring

Abstract: A method for monitoring machine conditions is based on machine learning through the use of a statistical model. A correlation coefficient is calculated using weights assigned to each sample that indicate the likelihood that that sample is an outlier. The resulting correlation coefficient is more robust against outliers. The calculation of the weight is based on the Mahalanobis distance from the sample to the sample mean. Additionally, hierarchical clustering is applied to intuitively reveal group information among sensors. By specifying a similarity threshold, the user can easily obtain desired clustering results.

Using Mutual Information for Adaptive Item Comparison and Student Assessment

Abstract: The author analyzes properties of mutual information between dichotomous concepts and test items. The properties generalize some common intuitions about item comparison, and provide principled foundations for designing item-selection heuristics for student assessment in computer-assisted educational systems. The proposed item-selection strategies along with some common and conceivable methods, including mutual information-based methods and Euclidean and Mahalanobis distance-based methods, for student classification are evaluated in a simulation-based environment. The simulator relies on Bayesian networks for capturing the uncertainty in students’ responses to test items. Simulated results indicate that the heuristics built upon the theoretical properties offer satisfactory performance profiles for item selection, and, not surprisingly, mutual information-based methods offer better performance for the task of student classification than distance-based methods.

Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images

Abstract: This study aimed to assess the spectral information potential of images captured with an unmanned aerial vehicle, in the context of crop–weed discrimination. A model is proposed in which the entire image acquisition chain is simulated in order to compute the digital values of image pixels according to several parameters (light, plant characteristics, optical filters, sensors…) to reproduce in-field acquisition conditions. The spectral mixings in the pixels are modeled, based on an image with a 60 mm spatial resolution, to estimate the impact of the resolution on the ability to discriminate small plants. The classification potential (i.e. the ability to separate two classes) in soil and vegetation and in monocotyledon and dicotyledon classes is studied using simulations for different vegetation rates (defined as the proportion of vegetation covering the surface projected in the considered pixel). The classification is unsupervised and based on the Mahalanobis distance computation. The results of soil-vegetation discrimination show that pixels with low vegetation rates can be classified as vegetation: pixels with vegetation rate greater than 0.5 had a probability to be correctly classified between 80 and 100%. Classification between monocotyledonous and dicotyledonous plants requires pixels with a high vegetation rate: to obtain a probability to be correctly classified better than 80%, vegetation rates in the pixels have to be over 0.9. To compare the results with data from real images, the same classification was tested on multispectral images of a weed infested field. The comparison confirmed the ability of the model to assess vegetation–soil and crop–weed discrimination potential for specific sensors (such as the multiSPEC 4C sensor, AIRINOV, Paris, France), where the acquisition chain parameters can be tested.