Metric learning is fundamental to lots of learning algorithms and it plays significant roles in many applications. In this paper, we present a LogDet divergence based metric learning approach to a learn Mahalanobis distance over input space of the instances. In the proposed model, the most natural constraint triplets are used as the labels of the training samples. Meanwhile, in order to avoid o...

In order to rationally evaluate the high speed railway operation safety level, the environmental safety evaluation index system of high speed railway should be well established by means of analyzing the impact mechanism of severe weather such as raining, thundering, lightning, earthquake, winding, and snowing. In addition to that, the attribute recognition will be identified to determine the si...

Assessing the parental genetic differences and their subsequent prediction of progeny performance is an important first step to assure the efficiency of any breeding program. In this study, we estimate the genetic divergence in Eucalyptus camaldulensis based on the morphological traits of 132 progenies grown in a savanna biome. Thus, a field experiment was performed using a randomized block des...

In our previous work we have shown that Mahalanobis kernels are useful for support vector classifiers both from generalization ability and model selection speed. In this paper we propose using Mahalanobis kernels for function approximation. We determine the covariance matrix for the Mahalanobis kernel using all the training data. Model selection is done by line search. Namely, first the margin ...

Distance-based supervised pattern recognition method (PRM) – discriminant analysis (DA) in principal component (PC) space described by Mahalanobis distance (MD) was suggested for definition of batch-to-batch dispersion of 22 drugs, produced by 14 manufacturers. The range of under test medicines numbered 18 pharmacological classes, including biologics, microbiological drugs and combined drugs, c...

We present a method for filtering noisy point clouds, specifically those constructed from merged depth maps as obtained from a range scanner or multiple view stereo (MVS), applying techniques that have previously been used in finding outliers in clustered data, but not in MVS or range scanning. We estimate the probability density function (PDF) over the space of observed points via a technique ...

The Mahalanobis distance is commonly used in multi-object trackers for measurement-to-track association. Starting with the original definition of the Mahalanobis distance we review its use in association. Given that there is no principle in multi-object tracking that sets the Mahalanobis distance apart as a distinguished statistical distance we revisit the global association hypotheses of multi...

Objective in this paper, we have done Mahalanobis Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. Methods In accordance with the strength of  wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we ...

How are we aware of our surroundings? Somehow, from a pair of tiny, two-dimensional pictures of the visual world – provided by our retinas – our brains are able to render the world as it is, accurately depicting the locations of both a distant mountain range and the camera we are pointing at it. To understand how space is represented in the brain, neurophysiologists have borrowed the useful con...