For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals we...

In this study, a Mahalanobis distance (MD)-based anomaly detection approach has been evaluated for non-punch through (NPT) and trench field stop (FS) insulated gate bipolar transistors (IGBTs). The IGBTs were subjected to electrical–thermal stress under a resistive load until their failure. Monitored on-state collector–emitter voltage and collector–emitter currents were used as input parameters...

The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces sever...

Small sample is an acute problem in many application domains, which may be partially addressed by feature selection or dimensionality reduction. For the purpose of distance learning, we describe a method for feature selection using equivalence constraints between pairs of datapoints. The method is based on L1 regularization and optimization. Feature selection is then incorporated into an existi...

Based on the reasoning expressed by Mahalanobis in his original article, the present article extends the Mahalanobis distance beyond the set of normal distributions. Sufficient conditions for existence and uniqueness are studied, and some properties derived. Since many statistical methods use the Mahalanobis distance as e vehicle, e.g. the method of least squares and the chi-square hypothesis t...

To classify time series by nearest neighbor, we need to specify or learn a distance. We consider several variations of the Mahalanobis distance and the related Large Margin Nearest Neighbor Classification (LMNN). We find that the conventional Mahalanobis distance is counterproductive. However, both LMNN and the class-based diagonal Mahalanobis distance are competitive.

Since the effectiveness of MT adaptation relies on the text repetitiveness, the question on how to measure repetitions in a text naturally arises. This work deals with the issue of looking for and evaluating text features that might help the prediction of the impact of MT adaptation on translation quality. In particular, the repetition rate metric, we recently proposed, is compared to other fea...

Abstract. We introduce an automated aerosol type classification method, called Source Classification Analysis (SCAN). SCAN is based on predefined and characterized source regions, the time that air parcel spends above each geographical region, a number of additional criteria. The output compared with two independent methods, which use intensive optical parameters from lidar data: (1) Mahalanobi...

In this paper, we investigate the asymptotic distributions of two types Mahalanobis distance (MD): leave-one-out MD and classical with both Gaussian- non-Gaussian-distributed complex random vectors, when sample size n dimension variables p increase under a fixed ratio c=p/n→∞. We distributional properties samples are independent, but not necessarily identically distributed. Some results regardi...