Image segmentation is the classification of data sets into group of similar data points. This article proposed a method to determine the winner unit by self organizing mapping network. The distance between the input vector and the weight vector has been determined by mahalanobis distance and chooses the unit whose weight vector has the smallest mahalanobis distance from the input vector. The re...

The Mahalanobis Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. The Mahalanobis Taguchi System is of interest because of its reported accuracy in forecasting small, correlated data sets. Th...

This paper contains three contributions to the problem of learning a Mahalanobis distance. First, a general framework for kernelizing Mahalanobis distance learners is presented. The framework allows existing algorithms to learn a Mahalanobis distance in a feature space associated with a pre-specified kernel function. The framework is then used for kernelizing three well-known learners, namely, ...

The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. Our Mahalanobis Distance Quality Control (MDQC) is a multivariate quality assessment method for microarrays that is based on the similarity of quality measures across arra...

In extending univariate outlier detection methods to higher dimension, various issues arise: limited visualization methods, inadequacy of marginal methods, lack of a natural order, limited parametric modeling, and, when using Mahalanobis distance, restriction to ellipsoidal contours. To address and overcome such limitations, we introduce nonparametric multivariate outlier identifiers based on m...

To classify time series by nearest neighbors, we need to specify or learn one or several distance measures. We consider variations of the Mahalanobis distance measures which rely on the inverse covariance matrix of the data. Unfortunately — for time series data — the covariance matrix has often low rank. To alleviate this problem we can either use a pseudoinverse, covariance shrinking or limit ...

The supervised self-organizing map consists in associating output vectors to input vectors through a map, after self-organizing it on the basis of both input and desired output given altogether. This paper compares the use of Euclidian distance and Mahalanobis distance for this model. The distance comparison is made on a data classification application with either global approach or partitionin...

The Mahalanobis-Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. This paper presents a comparison of the Mahalanobis-Taguchi System and a standard statistical technique for defect detection ...