Wedging spacetime principal null directions

Principal Directions-Based Pivot Placement

Determining a good sets of pivots is a challenging task for metric space indexing. Several techniques to select pivots from the data to be indexed have been introduced in the literature. In this paper, we propose a pivot placement strategy which exploits the natural data orientation in order to select space points which achieve a good alignment with the whole data to be indexed. Comparison with...

TITLE : MPD in Telomerase Null Mice PRINCIPAL

Determination of Principal Permeability Directions in Reservoir Rocks from Micro-CT Data

The routine measurement of direction-dependent reservoir rock properties like permeability often takes place along the axial direction of core samples. As permeability is a tensor property of porous materials, it should be fully described by a tensor matrix or by three main permeabilities in principal directions. Due to compaction, cementation, and other lithification processes, which take plac...

Semi-Supervised Learning through Principal Directions Estimation

We describe methods for taking into account unlabeled data in the training of a kernel-based classifier, such as a Support Vector Machines (SVM). We propose two approaches utilizing unlabeled points in the vicinity of labeled ones. Both of the approaches effectively modify the metric of the pattern space, either by using nonspherical Gaussian density estimates which are determined using EM, or ...

Learning principal directions: Integrated-squared-error minimization

A common derivation of principal component analysis (PCA) is based on the minimization of the squared-error between centered data and linear model, corresponding to the reconstruction error. In fact, minimizing the squared-error leads to principal subspace analysis where scaled and rotated principal axes of a set of observed data, are estimated. In this paper, we introduce and investigate an al...