Ana Isabel González

An analysis of the GLVQ algorithm

Generalized learning vector quantization (GLVQ) has been proposed in as a generalization of the simple competitive learning (SCL) algorithm. The main argument of GLVQ proposal is its superior insensitivity to the initial values of the weights (code vectors). In this paper we show that the distinctive characteristics of the definition of GLVQ disappear outside a small domain of applications. GLVQ becomes identical to SCL when either the number of code vectors grows or the size of the input space is large. Besides that, the behavior of GLVQ is inconsistent for problems defined on very small scale input spaces. The adaptation rules fluctuate between performing descent and ascent searches on the gradient of the distortion function.

A Sensitivity Analysis of the Self Organizing Maps as an AdaptiveOne-pass Non-stationary Clustering Algorithm: the Case of ColorQuantization of Image Sequences

In this paper we study the sensitivity of the Self Organizing Map to several parameters in the context of the one-pass adaptive computation of cluster representatives over non-stationary data. The paradigm of Non-stationary Clustering is represented by the problem of Color Quantization of image sequences.

Experiments of Fast Learning with High Order Boltzmann Machines

This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval. The absence of hidden units and the restriction to classification problems allows for the estimation of the connection statistics, without the computational cost involved in the application of simulated annealing. In this setting, the learning process can be sped up several orders of magnitude with no appreciable loss of quality of the results obtained.

An adaptive field rule for non-parametric MRI intensity inhomogeneity estimation algorithm

A widely accepted magnetic resonance imaging (MRI) model states that the observed voxel intensity is a piecewise constant signal intensity function corresponding to the tissue spatial distribution, corrupted with multiplicative and additive noise. The multiplicative noise is assumed to be a smooth bias field, it is called intensity inhomogeneity (IIH) field. Our approach to IIH correction is based on the definition of an energy function that incorporates some smoothness constraints into the conventional classification error function of the IIH corrected image. The IIH field estimation algorithm is a gradient descent of this energy function relative to the IIH field. We call it adaptive field rule (AFR). We comment on the likeness of our approach to the self-organizing map (SOM) learning rule, on the basis of the neighboring function that controls the influence of the neighborhood on each voxels IIH estimation. We discuss the convergence properties of the algorithm. Experimental results show that AFR compares well with state of the art algorithms. Moreover, the mean signal intensity corresponding to each class of tissue can be estimated from the image data applying the gradient descent of the proposed energy function relative to the intensity class means. We test several variations of this gradient descent approach, which embody diverse assumptions about available a priori information.

Financialisation and the economic crisis in Spain

The aim of this chapter is to analyse the relationship between the financial crisis and the real economic crisis in Spain. The main hypothesis put forward by is that financialisation, which lies at the root of the financial crisis, has also implied changes in the real and financial behaviour of private (and public) agents, which explain the extent and prolonged duration of the crisis in European and other advanced economies, in general, and in Spain in particular. With this aim in mind, we first analyse the financialisation process of the Spanish economy, and then its effects on households, non-financial corporations, and the external sector. Finally, we focus on the mistakes in the management of fiscal policy and in the management of the Spanish banking crisis that have helped to deepen the economic crisis.

The impact of the financial and economic crises on European Union member states

Although the Great Recession is a global phenomenon, with roots outside the European Union (EU), its impact has been deeper and longer lasting in the EU than elsewhere. However, the impact of the Great Recession has not been the same in all the European countries. The objective of this chapter is to analyse the different effects of the economic and financial crisis among the European Union member states, focusing on the behaviour of a number of real and financial variables since the year 2003 to evaluate the impact of the crisis. Thus, we will analyse the performance of 17 economic variables grouped into seven categories: economic activity, labour market, income distribution, inflation, balance of payments, public finances, and financial balance sheets of total economy and sectors.

Basic Competitive Neural Networks as Adaptive Mechanisms for Non-Stationary Colour Quantisation

In this paper we consider the application of two basic Competitive Neural Networks (CNN) to the adaptive computation of colour representatives on image sequences that show non-stationary distributions of pixel colours. The tested algorithms are the Simple Competitive Learning (SCL) algorithm and the Frequency-Sensitive Competitive Learning (FSCL) algorithm. Both, SCL and FCSL are the simplest adaptive methods based, respectively, on minimising the distortion and on the search for a uniform quantisation. The aim of this paper is to study several computational properties of these methods when applied to non-stationary clustering as adaptive vector quantisation algorithms. Non-stationary colour quantisation is, therefore, representative of the more general class of non-station-ary clustering problems. We expect our results to be meaningful for other algorithms that involve either the minimisation of the distortion or the search for uniform quantisers. We study experimentally the effect of the size of the image sample employed in the one-pass adaptation, their robustness to initial conditions, and the effect of local versus global scheduling of the learning rate.

A Comparison of Experimental Results with an Evolution Strategy and Competitive Neural Networks for Near Real-Time Color Quantization of Image Sequences

Color quantization of image sequences is a case of non-stationary clustering problem. The approach we adopt to deal with this kind of problems is to propose adaptive algorithms to compute the cluster representatives. We have studied the application of Competitive Neural Networks and Evolution Strategies to the one-pass adaptive solution of this problem. One-pass adaptation is imposed by the near real-time constraint that we try to achieve. In this paper we propose a simple and effective evolution strategy for this task. Two kinds of competitive neural networks are also applied. Experimental results show that the proposed evolution strategy can produce results comparable to that of competitive neural networks.

Competitive stochastic neural networks for Vector Quantization of images

Abstract A stochastic approximation to the nearest neighbour (NN) classification rule is proposed. This approximation is called Local Stochastic Competition (LSC). Some convergence properties of LSC are discussed, and experimental results are presented. The approach shows a great potential for speeding up the codification process, with an affordable loss of codification quality.

Derivation of SOM-like rules for intensity inhomogeneity correction in MRI

Given an appropriate imaging resolution, a common Magnetic Resonance Imaging (MRI) model assumes that the object under study is composed of piecewise constant materials, so that MRI produces piecewise constant images. The intensity inhomogeneity (IIH), due to the spatial inhomogeneity in the excitatory Radio Frequency (RF) signal and other effects, is modeled by a multiplicative inhomogeneity field. We propose and test two estimation rules of the IIH field, inspired in the Self Organizing Map (SOM), derived from well defined energy functions.