The estimation of covariance matrices multiple classes with limited training data is a difficult problem. sample matrix (SCM) known to perform poorly when the number variables large compared available samples. In order reduce mean squared error (MSE) SCM, regularized (shrinkage) SCM estimators are often used. this work, we consider (RSCM) for multiclass problems that couple together two differe...

In image processing, image similarity indices evaluate how much structural information is maintained by a processed image in relation to a reference image. Commonly used measures, such as mean squared error (MSE) and peak signal to noise ratio (PSNR), ignore the spatial information (e.g. redundancy) contained in natural images, which can lead to an inconsistent similarity evaluation from the hu...

Chemical modelling serves two purposes in dynamical models: accounting for the effect of microphysics on dynamics and providing observable signatures. Ideally, former must be done as part hydrodynamic simulation but this comes with a prohibitive computational cost which leads to many simplifications being used practice. To produce statistical emulator that replicates full chemical model capable...

The dispoportionate dropout rate of female college students from math, science, and engineering (MSE) fields has recently received much attention (Brainard, Laurich-McIntyre, 6' Moblqr, 1995; Carlin, 1997). The reasons far women 's higher attrition rate from MSE fields remain unclear. Eighty lstyear university students with apeexisting interest in MSE complehd a computer task-the Implicit Assoc...

the principle of dimensionality reduction with pca is the representation of the dataset ‘x’in terms of eigenvectors ei ∈ rn  of its covariance matrix. the eigenvectors oriented in the direction with the maximum variance of x in rn carry the most      relevant information of x. these eigenvectors are called principal components [8]. assume that n images in a set are originally represented in mat...