Supervised dimensionality reduction with tensor representation has attracted great interest in recent years. It has been successfully applied to problems with tensor data, such as image and video recognition tasks. However, in the tensor based methods, how to select the suitable dimensions is a very important problem. Since the number of possible dimension combinations exponentially increases w...

In this study, we present two different methods of multivariate analysis of voxel-based diffusion tensor imaging (DTI) data, using as an example data derived from 59 professional boxers and 12 age-matched controls. Conventional univariate analysis ignores much of the diffusion information contained in the tensor. Our first multivariate method uses the Hotelling's T(2) statistic and the second u...

Orthogonal Tensor Neighborhood Preserving Embedding (OTNPE) is an efficient dimensionality reduction algorithm for two-dimensional images. However, insufficiencies of the robustness performance and deficiencies of supervised discriminant information are remained. Motivated by the sparse learning, an algorithm called Orthogonal Tensor Sparse Neighborhood Embedding (OTSNPE) for two-dimensional im...

The Robustness of Facial Expression Recognition (FER) is based on information contained in color facial images. The Tensor Perceptual Color Framework (TPCF) enables multilinear image analysis in different color spaces. This demonstrates that the color components provide additional information for robust FER. By using this framework color components RGB, YCbCr, CIELab or CIELuv space of color im...

Background We aimed to identify the indicators of healthcare fraud and abuse in general physicians’ drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse.   Methods We applied data mining approach to a major health insurance organization dataset of private sector general physicians’ prescription claims. It involved 5 ste...

Suppose that X is a d-dimensional elliptically distributed random vector with dispersion matrix Σ . Many multivariate statistical methods like principal component analysis, canonical correlation analysis, linear discriminant analysis, and multivariate regression require the covariance or dispersion matrix of X only up to a scaling constant. Hence, the present work concentrates on the shape matr...

This paper describes a unified approach to the detection of point landmarks-whose neighborhoods convey discriminant information-including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application ...