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Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original...
This paper discusses the problem of clustering data changing over time, a research domain that is attracting increasing attention due to the increased availability of streaming data in the Web 2.0 era. In the analysis conducted throughout the paper we make use of the kernel spectral clustering with memory (MKSC) algorithm, which is developed in a constrained optimization setting. Since the objective...
In this paper we extend the agglomerative hierarchical kernel spectral clustering (AH-KSC [1]) technique from networks to datasets and images. The kernel spectral clustering (KSC) technique builds a clustering model in a primal-dual optimization framework. The dual solution leads to an eigen-decomposition. The clustering model consists of kernel evaluations, projections onto the eigenvectors and a...
The article introduces quantum clustering inspired from the quantum mechanics and extended to text analysis. This novel method upgrades the nonparametric density estimation and, different from the latter, quantum clustering constructs the potential function to determine the cluster center instead of the Gaussian kernel function. The result of a comparative experiment proves the advantage of quantum...
There have been a large number of external validity indices proposed for cluster validity. One such class of cluster comparison indices is the information theoretic measures, due to their strong mathematical foundation and their ability to detect non-linear relationships. However, they are devised for evaluating crisp (hard) partitions. In this paper, we generalize eight information theoretic crisp...
In this paper, an algorithm to automatically cluster the Self-Organizing Map (SOM) is presented. The proposed approach consists of creating a graph based on the SOM grid, whose connection strengths are measured in terms of pattern density. The connection of this graph are filtered in order to remove the mutually weakest connections between two adjacent neurons. The remaining graph is then pruned after...
In this paper we present a novel semi-supervised classification approach which combines bilinear formulation for non-parallel binary classifiers based upon Kernel Spectral Clustering. The cornerstone of our approach is a bilinear term introduced into the primal formulation of semi-supervised classification problem. In addition we perform separate manifold regularization for each individual classifier...
In this paper, we consider a batch learning algorithm for the multilayer neural network with multi-valued neurons (MLMVN) and its soft margins variant (MLMVN-SM). MLMVN is a neural network with a standard feedforward organization based on the multi-valued neuron (MVN). MVN is a neuron with complex-valued weights and inputs/output located on the unit circle. Standard MLMVN has a derivative-free learning...
Metalearning approach to the model selection problem - exploiting the idea that algorithms perform similarly on similar datasets - requires a suitable metric on the dataset space. One common approach compares the datasets based on fixed number of features describing the datasets as a whole. The information based on individual attributes is usually aggregated, taken for the most relevant attributes...
Multi-task learning improves generalization performance by learning several related tasks jointly. Several methods have been proposed for multi-task learning in recent years. Many methods make strong assumptions about symmetric task relationships while some are able to utilize externally provided task relationships. However, in many real world tasks the degree of relatedness among tasks is not known...
Classification and decision systems in data analysis are mostly based on accuracy optimization. This criterion is only a conditional informative value if the data are imbalanced or false positive/negative decisions cause different costs. Therefore more sophisticated statistical quality measures are favored in medicine, like precision, recall etc‥ Otherwise, most classification approaches in machine...
One of the key goals of biological Natural Language Processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependencies. We propose in this paper a hybrid constituency-dependency...
In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during...
Glioblastomas are one the most aggressive brain tumors. Their usual bad prognosis is due to the heterogeneity of their response to treatment and the lack of early and robust biomarkers to decide whether the tumor is responding to therapy. In this work, we propose the use of a semi-supervised methodology for source extraction to identify the sources representing tumor response to therapy, untreated/unresponsive...
The clinical management of brain tumors is very sensitive; thus, their non-invasive characterization is often preferred. Non-negative Matrix Factorization techniques have been successfully applied in the context of neuro-oncology to extract the underlying source signals that explain different tissue tumor types, for which knowing the number of sources to calculate was always required. In the current...
Alzheimer's Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders, and their investigation remains an open challenge. ElectroEncephalography (EEG) appears as a non-invasive and repeatable technique to diagnose brain abnormalities. Despite technical advances, the analysis of EEG spectra is usually carried out by experts that must...
This paper presents a new approach for relational data partitioning using the notion of dominant sets. A dominant set is a subset of data points satisfying the constraints of internal homogeneity and external in-homogeneity, i.e. a cluster. However, since any subset of a dominant set cannot be a dominant set itself, dominant sets tend to be compact sets. Hence, in this paper, we present a novel approach...
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