Biomedical subjects
Publications and source records attributed to Simone Fiori.
The aim of this paper is to present a unifying view of four SVD-neural-computation techniques found in the scientific literature and to present some theoretical results on their behavior. The considered SVD neural algorithms are shown to arise as Riemannian-gradient flows on double Stiefel manifold and their geometric and dynamical properties are investigated with the help of differential geometry.
The aim of this paper is to present an efficient implementation of unsupervised adaptive-activation function neurons dedicated to one-dimensional probability density estimation, with application to independent component analysis. The proposed implementation is a computationally light improvement to adaptive pseudo-polynomial neurons, recently presented in Fiori, S. (2000a). Blind signal processing by the adaptive activation function neurons. Neural Networks, 13(6), 597-611, and is based upon the concept of 'look-up table' (LUT) neurons.
In the present paper we investigate iterative minor subspace analysis computation by describing a neural approach based on weight flow on Stiefel manifold and by discussing four neural algorithms and a purely algebraic algorithm known from the scientific literature. A comparison of numerical experimental results and computational complexity estimates confirms the effectiveness and efficiency of the proposed approach.
This article investigated the behavior of a single-input, single-unit neuron model of the Bell-Sejnowski class, which learn through the maximum-entropy principle, in order to understand its probability density function and matching ability.
We present an overview of independent component analysis, an emerging signal processing technique based on neural networks, with the aim to provide an up-to-date survey of the theoretical streams in this discipline and of the current applications in the engineering area. We also focus on a particular application, dealing with a remote sensing technique based on synthetic aperture radar imagery processing: we briefly review the features and main applications of synthetic aperture radar and show how blind signal processing by neural networks may be advantageously employed to enhance the quality of remote sensing data.
The present paper aims at introducing the concepts and mathematical details of unsupervised neural learning with orthonormality constrains. The neural structures considered are single non-linear layers and the learnable parameters are organized in matrices, as usual, which gives the parameters spaces the geometrical structure of the Euclidean manifold. The constraint of orthonormality for the connection-matrices further restricts the parameters spaces to differential manifolds such as the orthogonal group, the compact Stiefel manifold and its extensions. For these reasons, the instruments for characterizing and studying the behavior of learning equations for these particular networks are provided by the differential geometry of Lie groups. In particular, two sub-classes of the general Lie-group learning theories are studied in detail, dealing with first-order (gradient-based) and second-order (non-gradient-based) learning. Although the considered class of learning theories is very general, in the present paper special attention is paid to unsupervised learning paradigms.