Improving piecewise linear separation incremental algorithms using complexity reduction methods

Derivation of a new criterion function based on an information measure for improving piecewise linear separation incremental algorithms

Improving the Performance of Piecewise Linear Separation Incremental Algorithms for Practical Hardware Implementations

In this paper we shall review the common problems associated with Piecewise Linear Separation incremental algorithms. This kind of neural models yield poor performances when dealing with some classification problems, due to the evolving schemes used to construct the resulting networks. So as to avoid this undesirable behavior we shall propose a modification criterion. It is based upon the defin...

Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images

Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...

Piecewiese linear source separation

We propose a new framework, called piecewise linear separation, for blind source separation of possibly degenerate mixtures, including the extreme case of a single mixture of several sources. Its basic principle is to : 1/ decompose the observations into “components” using some sparse decomposition/nonlinear approximation technique; 2/ perform separation on each component using a “local” separa...

Enhanced unit training for piecewise linear seperation incremental algorithms