Topic
Mahalanobis distance
About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.
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TL;DR: A decision model for the robot selection problem is proposed using both a robustified Mahalanobis distance analysis, i.e. a multivariate distance measure, and principal‐components analysis, and takes into consideration the fact that a robot′s performance, as specified by the manufacturer, is often unobtainable in reality.
Abstract: Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased, with many of these firms now offering a wide range of robots. A potential user is thus faced with many options in both performance and cost. Proposes a decision model for the robot selection problem using both a robustified Mahalanobis distance analysis, i.e. a multivariate distance measure, and principal‐components analysis. Unlike most other models for robot selection, this model takes into consideration the fact that a robot′s performance, as specified by the manufacturer, is often unobtainable in reality. The robots selected by the proposed model become candidates for factory testing to verify manufacturers′ specifications. Tests the proposed model on a real data set and presents an example.
42 citations
TL;DR: In this paper, principal component outlier detection methods are discussed and their application in the soft independent modelling of class analogy (SIMCA) method of pattern recognition is clarified and compared to allocation procedures based on the Mahalanobis distance.
Abstract: Principal component outlier detection methods are discussed and their application in the soft independent modelling of class analogy (SIMCA) method of pattern recognition is clarified. SIMCA is compared to allocation procedures based on the Mahalanobis distance. Finally, the differences between the SIMCA method and quadratic discriminant analysis are discussed. The discussion is illustrated with an example from spectroscopy.
42 citations
TL;DR: In this article, two classification approaches were investigated for the mapping of tropical forests from Landsat-TM data of a region north of Manaus in the Brazilian state of Amazonas.
Abstract: Two classification approaches were investigated for the mapping of tropical forests from Landsat-TM data of a region north of Manaus in the Brazilian state of Amazonas. These incorporated textural information and made use of fuzzy approaches to classification. In eleven class classifications the texture-based classifiers (based on a Markov random field model) consistently provided higher classification accuracies than conventional per-pixel maximum likelihood and minimum distance classifications, indicating that they are more able to characterize accurately several regenerating forest classes. Measures of the strength of class memberships derived from three classification algorithms (based on the probability density function, a posteriori probability and the Mahalanobis distance) could be used to derive fuzzy image classifications and be used in post-classification processing. The latter, involving either the summation of class memberships over a local neighbourhood or the application of homogene...
42 citations
01 Jan 2006
TL;DR: The paper compares the results of three advanced classifiers against a simple minimum distance classifier, and shows that while the simple classifier provides an error rate just over 6%, error rates down to 1-2% can be achieved with a combination of feature selection together with an advanced classsifier such as ant colony optimization.
Abstract: The pap-smear benchmark database provides data for comparing classification methods. The data consists of 917 images of pap-smear cells, classified carefully by cyto-technicians and doctors. The classes are difficult to separate, since class membership is not clearly defined. A basic data analysis provides numerical measures indicating how well the classes are separated, based on the Mahalanobis distance norm. The paper compares the results of three advanced classifiers against a simple minimum distance classifier. The results show that while the simple classifier provides an error rate just over 6%, error rates down to 1-2% can be achieved with a combination of feature selection together with an advanced classsifier such as ant colony optimization. Students and researchers can access the database via the Internet, and use it to test and compare their own classification methods.
42 citations
TL;DR: It is demonstrated that the minimum distance approaches the maximum distance even for some low dimensional distributions, such as normal distribution, and it is observed that the behavior of Euclidean distance becomes more useful with increased number of samples.
41 citations