Data mining applications usually encounter high dimensional data spaces. Most of these dimensions contain ‘uninteresting’ data, which would not only be of little value in terms of discovery of any rules or patterns, but have been shown to mislead some classification algorithms. Since, the computational effort increases very significantly (usually exponentially) in the presence of a large number...

A greedy algorithm for the construction of a reduced model with reduction in both parameter and state is developed for efficient solution of statistical inverse problems governed by partial differential equations with distributed parameters. Large-scale models are too costly to evaluate repeatedly, as is required in the statistical setting. Furthermore, these models often have high dimensional ...

We show that Haldanes new definition of statistics, when generalised to infinite dimensional Hilbert spaces, is equal to the high temperature limit of the second virial coefficient. We thus show that this exclusion statistics parameter, g , of anyons is non-trivial and is completely determined by its exchange statistics parameter α. We also compute g for quasiparticles in the Luttinger model an...

in this paper we define intuitionistic fuzzy metric and normedspaces. we first consider finite dimensional intuitionistic fuzzy normed spacesand prove several theorems about completeness, compactness and weak convergencein these spaces. in section 3 we define the intuitionistic fuzzy quotientnorm and study completeness and review some fundamental theorems. finally,we consider some properties of...

In this paper the characteristics of unsteady three-dimensional incompressible flows with heat transfer are obtained along with artificial compressibility of Chorin. At first, compatibility equations and pseudo characteristics for three-dimensional flows are derived from five governing equations (continuity equation, Momentum equations in three directions, and energy equation) and then results ...

parameter estimation is an important task in the modeling, classification, and detection of radar clutters. radar clutters have stochastic characteristics. therefore, statistical distributions are usually used to describe the features of clutters better. k distribution is one of the most common models utilized to the simulation of clutters. this distribution, which consists of scale and shape p...

We propose a method for simultaneous shape-constrained segmentation and parameter recovery. The parameters can describe anything from 3D shape to 3D pose and we place no restriction on the topology of the shapes, i.e. they can have holes or be made of multiple parts. We use Shared Gaussian Process Latent Variable Models to learn multimodal shape-parameter spaces. These allow non-linear embeddin...