Mahalanobis distance

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

Online classification of lung sounds using DSP

Abstract: A real-time diagnosis system, based on Motorola's 56311 digital signal processor (DSP), is used for the classification of lung sounds into two classes: healthy and pathological. The instrument has two inputs the first of which is from a microphone placed on the chest of the patient while the other is from a flowmeter that is used to label the lung sounds as belonging to the inspiration or expiration cycle. The sampled lung sound of a full respiration cycle is divided into 6 phases with the help of the flowmeter signal, and each phase is divided further into 10 overlapping segments. Each segment is modeled by an auto regressive (AR) model of order 6 by means of the Levinson-Durbin algorithm. The classification process is done using two classifiers: k-nearest neighbor (k-NN) classifier with Itakura and Euclidean distance measures, and minimum distance classifier with the Mahalanobis distance measure. The software was written entirely in assembly language and the result of the classification process is displayed on a character display (LCD).

Predictable Components and Singular Vectors

Abstract: This paper shows that if a measure of predictability is invariant to affine transformations and monotonically related to forecast uncertainty, then the component that maximizes this measure for normally distributed variables is independent of the detailed form of the measure. This result explains why different measures of predictability such as anomaly correlation, signal-to-noise ratio, predictive information, and the Mahalanobis error are each maximized by the same components. These components can be determined by applying principal component analysis to a transformed forecast ensemble, a procedure called predictable component analysis (PrCA). The resulting vectors define a complete set of components that can be ordered such that the first maximizes predictability, the second maximizes predictability subject to being uncorrelated of the first, and so on. The transformation in question, called the whitening transformation, can be interpreted as changing the norm in principal component analysis. ...

Mahalanobis distance-based ARTMAP network

Abstract: A new ARTMAP-based network is proposed, which is in part a generalization of Williamson's Gaussian ARTMAP. The training of the new network is based on activation and match functions that are equal and identical to Mahalanobis distance. The classification treats the clusters obtained through training as Gaussian mixture models. The training process has improved its efficiency due to the fact that the repeated searches for the resonant node have been eliminated. In addition, the inverse covariance matrices are computed recurrently. The new network is analyzed and compared with the fuzzy ARTMAP and Gaussian ARTMAP. The results from the new network have shown much better hit rates at fewer output nodes on several benchmark problems. A complexity analysis of the three networks is also provided.

A beta partial least squares regression model: Diagnostics and application to mining industry data

Abstract: We propose a methodology based on partial least squares (PLS) regression models using the beta distribution, which is useful for describing data measured between zero and one. The beta PLS model parameters are estimated with the maximum likelihood method, whereas a randomized quantile residual and the generalized Cook and Mahalanobis distances are considered as diagnostic methods. A simulation study is provided for evaluating the performance of these diagnostic methods. We illustrate the methodology with real-world mining data. The results obtained in this study based on the beta PLS model and its diagnostics may be of interest for the mining industry.