This paper explores the use of multi-dimensional trees to provide spatial and temporal e ciencies in imaging large data sets. Each node of the tree contains a model of the data in...
A new method is proposed for the blind subspace-based identification of the coefficients of time-varying (TV) single-input multiple-output (SIMO) FIR channels. The TV channel coef...
This paper introduces a technique for the numerical generation of basis functions that are capable of parameterizing the frequency-variant nature of cross-sectional conductor curre...
Xin Hu, Tarek Moselhy, Jacob K. White, Luca Daniel
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
Abstract. We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was a...
Te-Won Lee, Thomas Wachtler, Terrence J. Sejnowski
Techniques in numerical simulation such as the finite element method depend on basis functions for approximating the geometry and variation of the solution over discrete regions ...
— This work addresses the problem of selecting a subset of basis functions for a model linear in the parameters for regression tasks. Basis functions from a set of candidates are...
—A procedure is presented for selecting and ordering the polynomial basis functions in the functional link net (FLN). This procedure, based upon a modified Gram Schmidt orthonorm...
Regression or least squares fitting is an important problem in statistics, data mining and many other applications. In recent years, basis functions derived from the underlying g...
The conventional method of generating a basis that is optimally adapted (in MSE) for representation of an ensemble of signals is Principal Component Analysis (PCA). A more ambitio...
Rosa M. Figueras i Ventura, Umesh Rajashekar, Zhou...