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, ...

Abstract Methadone Flexi Dispensing Service (MFlex) has been officially re-branded from 1Malaysia (M1M) since 2nd January 2019. Patients under MFlex are frequently taking their methadone according to a plan provided by pharmacist at public clinic. From the dose monitoring taken annually, pharmacists can predict critical patients based on high monthly increases. However, current system is writte...

the organizations use all appropriate tools to improve their service to the customers. the detection of especial customers and the forecast of their behavior undoubtedly can play an important role in improvement of service. in this paper, a new statistical method called the mahalanobis taguchi system has been used for this purpose. this method is used for the analysis of real data of an insuran...

This paper presents an innovative diagnostic approach that includes detection and fault isolation using the Mahalanobis distance (MD). The fault isolation approach is based on the analysis of residual MD values corresponding to performance parameters. The residual value is calculated by taking the differences between MD values estimated in two different scenarios: first, when a performance para...

In this paper, drills wear monitoring method that based on the mahalanobis distance (MD) of spectrum energy features form cutting torque is proposed. The method consists of three steps. First, using the wavelet transform carried out separating cutting torque components from the original signals. Second, signals feature is adopted to Welch spectrum method extracted on wavelet coefficients of dif...

Recently, Independent Component Analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of patterns. However, the effectiveness of features extracted by ICA (ICA features) has not been verified yet. As one of the reasons, it is considered that ICA features are obtained by increasing their independence rather than by increasing their class sep...

This paper describes automatic detection and classification of visual symptoms affected by fungal disease. Algorithms are developed to acquire and process color images of fungal disease affected on commercial crops like chili, cotton and sugarcane. The developed algorithms are used to preprocess, segment, extract and reduce features from fungal affected parts of a crop. The feature extraction i...

The reliability of power packs is very important for the performance of electronic equipment and ensuring the reliability of power electronic circuits is especially vital for equipment security. An alteration in the converter component parameter can lead to the decline of the power supply quality. In order to effectively prevent failure and estimate the remaining useful life (RUL) of superbuck ...

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...

In this work, we propose a scalable and fast algorithm to learn a Mahalanobis distance metric. The key issue in this task is to learn an optimal Mahalanobis matrix in this distance metric. It has been shown in the statistical learning theory [?] that increasing the margin between different classes helps to reduce the generalization error. Hence, our algorithm formulates the Mahalanobis matrix a...