In this contribution, we show that the incorporation of nonlinear dynamical measures into a multivariate discrimination provides a signal classification system that is robust to additive noise. The signal library was composed of nine groups of signals. Four groups were generated computationally from deterministic systems (van der Pol, Lorenz, Rössler and Hénon). Four groups were generated compu...

PURPOSE To develop and compare the ability of several automated classifiers to differentiate between normal and glaucomatous eyes based on the quantitative assessment of summary data reports from Stratus optical coherence tomography (OCT; Carl Zeiss Meditec Inc., Dublin, CA) in a Chinese population in Taiwan. METHODS One randomly selected eye from each of 89 patients with glaucoma and each of...

The use of the Mahalanobis distance in a lookup table approach to retrieval of in-water Inherent Optical Properties (IOPs) led to significant improvements in the accuracy of the retrieved IOPs, as high as 50% in some cases, with an average improvement of 20% over a wide range of case II waters. Previous studies have shown that inherent noise in hyperspectral data can cause significant errors in...

This paper discusses the application of the newly developed Mahalanobis distance-based ARTMAP (MART) network to the predication of sickle cell anemia patients’ response to Hydroxyurea treatment. Mahalanobis distance-based ARTMAP is a modified version of the Fuzzy ARTMAP networks where the activation and choice function are the same and equal to the Mahalanobis distance between the pattern and t...

The covariance matrix in the Mahalanobis distance can be trained by semi-definite programming, but training for a large size data set is inefficient. In this paper, we constrain the covariance matrix to be diagonal and train Mahalanobis kernels by linear programming (LP). Training can be formulated by ν-LP SVMs (support vector machines) or regular LP SVMs. We clarify the dependence of the solut...

Within the framework of kernel methods, linear data methods have almost completely been extended to their nonlinear counterparts. In this paper, we focus on nonlinear kernel techniques based on the Mahalanobis distance. Two approaches are distinguished here. The first one assumes an invertible covariance operator, while the second one uses a regularized covariance. We discuss conceptual and exp...

Class overlapping is one of the bottlenecks in data mining and pattern recognition, and affects the classification accuracy and generalization ability directly. In Mahalanobis-Taguchi System (MTS), the normal samples are used to construct reference space, while the abnormal samples are used to verify the validity of the reference space. If there is a class overlapping between the normal samples...

In this paper, we address the problem of efficient k-NN classification. In particular, in the context of Mahalanobis metric learning. Mahalanobis metric learning recently demonstrated competitive results for a variety of tasks. However, such approaches have two main drawbacks. First, learning metrics requires often to solve complex and thus computationally very expensive optimization problems. ...