A new methodology for clustering multivariate time-series data is proposed. The methodology is based on calculation of the degree of similarity between multivariate time-series datasets using two similarity factors. One similarity factor is based on principal component analysis and the angles between the principal component subspaces while the other is based on the Mahalanobis distance between ...

Predict and prevent maintenance is routinely carried out. However, how to address the problem of performance assessment maximizing the use of available monitoring data, and how to build a framework that integrates performance assessment, fault detection, and diagnosis are still a significant challenge. For this purpose, this article introduces an approach to performance assessment and fault dia...

The technology of near-infrared spectroscopy analysis is one of the important ways to test agricultural products inside quality quantitatively. The nearinfrared spectral analysis system which used the technology of sound and light tunable filter (AOTF) can be applied to on-line measurement. The system constructed by AOTF, analysis optimization wavelength together with genetic algorithm, execute...

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...

Outlier detection based on the Mahalanobis distance (MD) requires an appropriate transformation in case of compositional data. For the family of logratio transformations (additive, centered and isometric logratio transformation) it is shown that the MDs based on classical estimates are invariant to these transformations, and that the MDs based on affine equivariant estimators of location and co...

In this work we investigate kernel methods for object detection. Kernel principal component analysis (KPCA) is the algorithm used to generate nonlinear models of object variation. We focus on edge-based features, i.e. shapes, to represent objects. Object variations are learned a priori in feature space. Based on the Mahalanobis distance we define a distance measure for an object. In contrast to...

This paper evaluates how biologically meaningful landmarks and their geometry extracted from face images can be used for face recognition. The traditional Procrustes distance is studied for the landmark-based face model. By using complex principal component analysis, we propose a refined Procrustes distance that incorporates statistical correlation of landmarks. Motivated by research results fr...

Similarity search and data mining often rely on distance or similarity functions in order to provide meaningful results and semantically meaningful patterns. However, standard distance measures like Lp-norms are often not capable to accurately mirror the expected similarity between two objects. To bridge the so-called semantic gap between feature representation and object similarity, the distan...