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At present all commercially available digital photogrammetric software products are utilizing points to solve the problem of image orientation. Photogrammetry based on linear features is still at th...
Quasi-linear perturbations of a two-dimensional flow with a first integral and the corresponding parabolic PDE’s with a small parameter at the second order derivatives are considered in th...
A model of a strip of cardiac tissue consisting of a one-dimensional chain of cardiac units is derived in the form of a non-linear partial difference equation. Perturbation analysis is performed on th...
We establish that a non-Gaussian nonparametric regression model is asymptotically equivalent to a regression model with Gaussian noise. The approximation is in the sense of Le Cam's de®- ciency d...
A large number of online services provide automated recommendations to help users to navigate through a large collection of items. New items (products, videos, songs, advertisements) are suggested on ...
We consider the CDMA (code-division multipleaccess) multi-user detection problem for binary signals and additive white gaussian noise. We propose a spreading sequences scheme based on random sparse si...
Finding the sparsest solution to underdetermined systems of linear equations y = Φx is NP-hard in general. We show here that for systems with ‘typical’/‘random’ Φ, a good approximation to the sparse...
Consider an underdetermined system of linear equations y = Ax with known d×n matrix A and known y. We seek the sparsest nonnegative solution, i.e. the nonnegative x with fewest nonzeros satisfying y...
Consider a d × n matrix A, with d < n. The problem of solving for x in y = Ax is underdetermined, and has many possible solutions (if there are any). In several fields it is of interest to ...
We consider linear equations y = Φα where y is a given vector in R n , Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ Rm. We suppose that the columns of Φ are normalized ...
We introduce a technique for extending the classical method of Linear Discriminant Analysis to data sets where the predictor variables are curves or functions. This procedure, which we call functional...
We study linear smoothers and their use in building nonparametric regression models. In the first part of this paper we examine certain aspects of linear smoothers for scatterplots; examples of these ...
An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we intro...
We introduce a path following algorithm for L1-regularized generalized linear models. The L 1-regularization procedure is useful especially because it, in effect, selects variables according to the am...
In this paper, we introduce a modified version of linear discriminant analysis, called the “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of the “near...

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