Particle swarm optimization for multimodal functions: a clustering approach

The particle swarm optimization (PSO) algorithm is designed to find a single optimal solution and needs some modifications to be able to locate multiple optima on a multimodal function. In parallel with evolutionary computation algorithms, these modifications can be grouped in the framework of niching. In this work, we present a new approach to niching in PSO based on clustering particles to identify niches. The neighborhood structure, on which particles rely for communication, is exploited together with the niche information to locate multiple optima in parallel. Our approach was implemented in thek-means-based PSO (kPSO), which employs the standardk-means clustering algorithm, improved with a mechanism to adaptively identify the number of clusters.kPSO proved to be a competitive solution when compared with other existing algorithms, since it showed better performance on a benchmark set of multimodal functions

Knowledge discovery in databases of biomechanical variables: application to the sit to stand motor task

BACKGROUND: The interpretation of data obtained in a movement analysis laboratory is a crucial issue in clinical contexts. Collection of such data in large databases might encourage the use of modern techniques of data mining to discover additional knowledge with automated methods. In order to maximise the size of the database, simple and low-cost experimental set-ups are preferable. The aim of this study was to extract knowledge inherent in the sit-to-stand task as performed by healthy adults, by searching relationships among measured and estimated biomechanical quantities. An automated method was applied to a large amount of data stored in a database. The sit-to-stand motor task was already shown to be adequate for determining the level of individual motor ability. METHODS: The technique of search for association rules was chosen to discover patterns as part of a Knowledge Discovery in Databases (KDD) process applied to a sit-to-stand motor task observed with a simple experimental set-up and analysed by means of a minimum measured input model. Selected parameters and variables of a database containing data from 110 healthy adults, of both genders and of a large range of age, performing the task were considered in the analysis. RESULTS: A set of rules and definitions were found characterising the patterns shared by the investigated subjects. Time events of the task turned out to be highly interdependent at least in their average values, showing a high level of repeatability of the timing of the performance of the task. CONCLUSIONS: The distinctive patterns of the sit-to-stand task found in this study, associated to those that could be found in similar studies focusing on subjects with pathologies, could be used as a reference for the functional evaluation of specific subjects performing the sit-to-stand motor task

Convergence Behavior of Competitive Repetition-Suppression Clustering

Competitive Repetition-suppression (CoRe) clustering is a bio-inspired learning algorithm that is capable of automatically determining the unknown cluster number from the data. In a previous work it has been shown how CoRe clustering represents a robust generalization of rival penalized competitive learning (RPCL) by means of M-estimators. This paper studies the convergence behavior of the CoRe model, based on the analysis proposed for the distance-sensitive RPCL (DSRPCL) algorithm. Furthermore, it is proposed a global minimum criterion for learning vector quantization in kernel space that is used to assess the correct location property for the CoRe algorithm

Expansive competitive learning for kernel vector quantization

In this paper we present a necessary and sufficient condition for global optimality of unsupervised Learning Vector Quantization (LVQ) in kernel space. In particular, we generalize the results presented for expansive and competitive learning for vector quantization in Euclidean space, to the general case of a kernel-based distance metric. Based on this result, we present a novel kernel LVQ algorithm with an update rule consisting of two terms: the former regulates the force of attraction between the synaptic weight vectors and the inputs: the latter, regulates the repulsion between the weights and the center of gravity of the dataset. We show how this algorithm pursues global optimality of the quantization error by means of the repulsion mechanism. Simulation results are provided to show the performance of the model on common image quantization tasks: in particular, the algorithm is shown to have a superior performance with respect to recently published quantization models such as Enhanced LBG and Adaptive Incremental LB

Dynamical Neural Networks Construction for Processing of Labeled Structures

We show how Labeling RAAM (LRAAM) can be exploited to generate `on the fly' neural networks for associative access of labeled structures. The topology of these networks, that we call Generalized Hopfield Networks (GHN), depends on the topology of the query used to retrieve information, and the weights on the networks' connections are the weights of the LRAAM encoding the structures. A method for incremental discovering of multiple solutions to a given query is presented. This method is based on terminal repellers, which are used to `delete' known solutions from the set of admissible solutions to a query. Terminal repellers are also used to implement exceptions at query level, i.e., when a solution to a query must satisfy some negative constraints on the labels and/or substructures. Besides, the proposed model solves very naturally the connectionist variable binding problem at query level. Some results for a tree-like query are presented. Finally, we define a parallel mode of execution, exploiting terminal repellers, for the GHN, and we propose to use terminal attractors for implementing shared variables and graph queries

Supervised Neural Networks for the Classification of Structures

none2Until now neural networks have been used for classifying unstructured patterns and sequences, However, standard neural networks and statistical methods are usually believed to be inadequate when dealing with complex structures because of their feature-based approach, In fact, feature-based approaches usually fail to give satisfactory solutions because of the sensitivity of the approach to the a priori selection of the features, and the incapacity to represent any specific information on the relationships among the components of the structures, However, we show that neural networks can, in fact, represent and classify structured patterns, The key idea underpinning our approach is the use of the so called ''generalized recursive neuron,'' which is essentially a generalization to structures of a recurrent neuron, By using generalized recursive neurons, all the supervised networks developed for the classification of sequences, such as backpropagation through time networks, realtime recurrent networks, simple recurrent networks, recurrent cascade correlation networks, and neural trees can, on the whole, be generalized to structures. The results obtained by some of the above networks (with generalized recursive neurons) on the classification of logic terms are presented.noneA. SPERDUTI; STARITA A.Sperduti, Alessandro; Starita, A

A Robust Bio-Inspired Clustering Algorithm for the Automatic Determination of Unknown Cluster Number

The paper introduces a robust clustering algorithm that can automatically determine the unknown cluster number from noisy data without any a-priori information. We show how our clustering algorithm can be derived from a general learning theory, named CoRe learning, that models a cortical memory mechanism called repetition suppression. Moreover, we describe CoRe clustering relationships with Rival Penalized Competitive Learning (RPCL), showing how CoRe extends this model by strengthening the rival penalization estimation by means of robust loss functions. Finally, we present the results of simulations concerning the unsupervised segmentation of noisy images

Feature-wise Competitive Repetition Suppression Learning for Gene Data Clustering and Feature Ranking

The paper extends Competitive Repetition-suppression (CoRe) learning to deal with high dimensional data clustering. We show how CoRe can be applied to the automatic detection of the unknown cluster number and the simultaneous ranking of the features according to learned relevance factors. The effectiveness of the approach is tested on two freely available data sets from gene expression data and the results show that CoRe clustering is able to discover the true data partitioning in a completely unsupervised way, while it develops a feature ranking that is consistent with the state-of-the-art lists of gene relevance