186,374 research outputs found
Applying the possibilistic C-means algorithm in kernel-induced spaces
No full textIn this paper, we study a kernel extension of the classic possibilistic c-means. In the proposed extension, we implicitly map input patterns into a possibly high-dimensional space by means of positive semidefinite kernels. In this new space, we model the mapped data by means of the possibilistic clustering algorithm. We study in more detail the special case where we model the mapped data using a single cluster only, since it turns out to have many interesting properties. The modeled memberships in kernel-induced spaces yield a modeling of generic shapes in the input space. We analyze in detail the connections to one-class support vector machines and kernel density estimation, thus, suggesting that the proposed algorithm can be used in many scenarios of unsupervised learning. In the experimental part, we analyze the stability and the accuracy of the proposed algorithm on some synthetic and real datasets. The results show high stability and good performances in terms of accuracy
Special Session on Bioinformatics and Biostatistics withcontributions by: P. Fariselli et al., G. Cuda et al., G. Antoniol et al., D. Malchioldi et al., C. Chennubhotla et al., A. Eleuteri et al., F. Marangoni et al., F. Masulli et al., A. Micheli et al., G. Valentini
No full text
Workshop on: Soft computing and pattern recognition, with contributions by: D. Alfonso, F. Masulli and A. Sperduti, J. Linag, F. V.Jensen and H. I. Christens, H. H. Bothe and E. A. Wieden, E. Alpaydin
No full text
Fuzzy Clustering for Exploratory Analysis of EEG Event-Related Potentials
No full textWe introduce an analysis method for electroencephalography (EEG) data, focused on Event-Related Potentials (ERPs). Our approach is unsupervised and makes use of a fuzzy clustering algorithm based on the possibilistic framework, and includes a data-driven noise and artifact rejection phase. Our contribution provides a general analysis tool, applicable to any ERP data set, which can uncover the data set's internal structure. The fuzzy clustering algorithm is the core of our method, since its fine-grained membership grades how much a sample belongs to a given cluster, making the method applicable even when groups have a certain overlap. Prior to the clustering step, we apply weights to the feature vectors, optimizing them in order to enhance the variance within the dataset, and we extract time-window interval based features inspired by interval arithmetic. We apply the data processing workflow to the analysis a set of ERPs recorded during an emotional Go/NoGo task. We evaluate the performance of the unsupervised analysis by computing a measure based on the clusterization rate of trials in different experimental conditions. The results on the studied data set show that the proposed method obtains a difference of clusterization rate of 69% in Go vs. NoGo trials, when weights and interval-features are applied to the data, improving previous work not including weights and interval-features which had a rate of 31%. Furthermore, when compared with the standard Fuzzy c-means, our proposed possibilistic clustering algorithm outperforms it in terms of clusterization rate. We also examine the effect of pre-processing the data with Independent Component Analysis and removing noise-related components, and observe that this does not improve significantly the obtained results. These findings demonstrate that our proposed method provides a valuable data processing workflow robust to EEG artifacts and able to produce a clustering that is coherent with the experimental conditions represented in the ERP dataset
Unsupervised Analysis of Event-Related Potentials (ERPs) During an Emotional Go/NoGo Task
No full text
- …
