Search results for "basi"

showing 10 items of 12854 documents

Spectral clustering with the probabilistic cluster kernel

2015

Abstract This letter introduces a probabilistic cluster kernel for data clustering. The proposed kernel is computed with the composition of dot products between the posterior probabilities obtained via GMM clustering. The kernel is directly learned from the data, is parameter-free, and captures the data manifold structure at different scales. The projections in the kernel space induced by this kernel are useful for general feature extraction purposes and are here exploited in spectral clustering with the canonical k-means. The kernel structure, informative content and optimality are studied. Analysis and performance are illustrated in several real datasets.

business.industryCognitive NeurosciencePattern recognitionKernel principal component analysisComputer Science ApplicationsComputingMethodologies_PATTERNRECOGNITIONKernel methodArtificial IntelligenceVariable kernel density estimationKernel embedding of distributionsString kernelKernel (statistics)Radial basis function kernelArtificial intelligenceTree kernelbusinessMathematicsNeurocomputing
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Fuzzy sigmoid kernel for support vector classifiers

2004

This Letter proposes the use of the fuzzy sigmoid function presented in (IEEE Trans. Neural Networks 14(6) (2003) 1576) as non-positive semi-definite kernel in the support vector machines framework. The fuzzy sigmoid kernel allows lower computational cost, and higher rate of positive eigenvalues of the kernel matrix, which alleviates current limitations of the sigmoid kernel.

business.industryCognitive NeurosciencePattern recognitionSigmoid functionFuzzy logicComputer Science ApplicationsSupport vector machineKernel methodArtificial IntelligencePolynomial kernelKernel embedding of distributionsRadial basis function kernelLeast squares support vector machineArtificial intelligencebusinessMathematicsNeurocomputing
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Feature extraction from remote sensing data using Kernel Orthonormalized PLS

2007

This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.

business.industryComputer scienceFeature extractionContext (language use)Regression analysisPattern recognitionSparse approximationcomputer.software_genreKernel principal component analysisKernel (linear algebra)Kernel embedding of distributionsKernel (statistics)Radial basis function kernelArtificial intelligenceData miningbusinesscomputerRemote sensing2007 IEEE International Geoscience and Remote Sensing Symposium
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Three-dimensional object detection under arbitrary lighting conditions

2006

A novel method of 3D object recognition independent of lighting conditions is presented. The recognition model is based on a vector space representation using an orthonormal basis generated by the Lambertian reflectance functions obtained with distant light sources. Changing the lighting conditions corresponds to multiplying the elementary images by a constant factor and because of that, all possible lighting views will be elements that belong to that vector space. The recognition method proposed is based on the calculation of the angle between the vector associated with a certain illuminated 3D object and that subspace. We define the angle in terms of linear correlations to get shift and i…

business.industryComputer scienceMaterials Science (miscellaneous)3D single-object recognitionCognitive neuroscience of visual object recognitionInformation Storage and RetrievalReproducibility of ResultsImage EnhancementSensitivity and SpecificityFacial recognition systemIndustrial and Manufacturing EngineeringObject detectionPattern Recognition AutomatedLambertian reflectanceImaging Three-DimensionalOpticsArtificial IntelligenceImage Interpretation Computer-AssistedOrthonormal basisBusiness and International ManagementbusinessAlgorithmsLightingSubspace topologyApplied Optics
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A novel method for network intrusion detection based on nonlinear SNE and SVM

2017

In the case of network intrusion detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal intrusion detection system (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem of this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimise the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE-based oversampling of minority classes. In order to alleviate the issue arising from the curse…

business.industryComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingDimensionality reductionFeature vectorPattern recognitionGeneral MedicineIntrusion detection systemSupport vector machineBenchmark (computing)EmbeddingRadial basis functionArtificial intelligencebusinessCurse of dimensionality
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Problematic internet use prior to and during the COVID-19 pandemic

2021

The health and socio-economic challenges arising from the COVID-19 pandemic have led to greater reliance on the internet to meet basic needs and responsibilities. Greater engagement in online activities may have negative mental and physical health consequences for some vulnerable individuals, particularly under mandatory self-isolation or ‘lockdown’ conditions. The present study investigated whether changes in levels of involvement in online activities during the COVID-19 pandemic (i.e., watching TV series,online sexual activities, video games, social networks, gambling, online shopping, and instant messaging) were associated with problematic internet use, as well as whether certain psychol…

business.industryCross-sectional studyCommunicationCOVID-19online activityContext (language use)Minor (academic)Affect (psychology)ImpulsivityCovid-19; Internet Use; Gaming; TV series; Cybersex; Social Network SitesPathology and Forensic Medicineproblematic internet usePandemicmedicinerisk factorscross-sectional studyThe InternetBasic needsmedicine.symptombusinessPsychologyGeneral PsychologySocial Sciences (miscellaneous)Information SystemsClinical psychologyCyberpsychology: Journal of Psychosocial Research on Cyberspace
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Semisupervised kernel orthonormalized partial least squares

2012

This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…

business.industryFeature extractionNonlinear dimensionality reductionPattern recognitionComputingMethodologies_PATTERNRECOGNITIONKernel methodVariable kernel density estimationKernel (statistics)Radial basis function kernelPartial least squares regressionArtificial intelligenceCluster analysisbusinessMathematics2012 IEEE International Workshop on Machine Learning for Signal Processing
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Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis

2014

This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. Th…

business.industryFeature extractionPattern recognitioncomputer.software_genreKernel principal component analysisComputingMethodologies_PATTERNRECOGNITIONKernel embedding of distributionsPolynomial kernelVariable kernel density estimationKernel (statistics)Radial basis function kernelGeneral Earth and Planetary SciencesPrincipal component regressionData miningArtificial intelligenceElectrical and Electronic EngineeringbusinesscomputerMathematicsRemote sensingIEEE Transactions on Geoscience and Remote Sensing
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Perfluorinated Compounds Distribution and Source Identification in Sediments of Lake Victoria Gulf Basin

2011

Perfluorooctanoic acid and perfluorooctane sulfonate were determined in the sediments from Winam Gulf, which is in the Kenyan side of Lake Victoria and in its source rivers. The sources of perfluorinated compounds within the Gulf of Lake Victoria have been identified and their levels determined for the first time, in this study, using SPE and HPLC-MS-MS analytical methodology. Variability in the concentrations of perfluorooctanoic acid and perfluorooctane sulfonate ranged from 1.4–99.1 and <1–57.5 ng/g in river sediments, respectively, which was higher than concentrations obtained from lake sediments (range perfluorooctanoic acid <1–24.1 ng/g and perfluorooctane sulfonate <1–4.0 ng/g). The …

business.industryHealth Toxicology and MutagenesisSoil ScienceSewageStructural basinPollutionIndustrial wastePerfluorooctanechemistry.chemical_compoundchemistryEnvironmental chemistryLc ms msEnvironmental ChemistryEnvironmental sciencePerfluorooctanoic acidWater treatmentbusinessSoil and Sediment Contamination: An International Journal
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What Is a ‘Digital Family’?

2019

This chapter introduces the concept of the digital family. Digital families are one form of distributed extended families, consisting of related individuals living in one or more households who utilize at least basic level information and communication technologies and social media applications to stay connected and maintain a sense of unity. The strengths and limitations of the notion are discussed, assessing its usefulness vis-a-vis neighbouring concepts. The chapter ends with the discussion of the perception of family in the three countries studied, Finland, Italy and Slovenia, and of the differences found between them.

business.industryInformation and Communications TechnologyPerceptionmedia_common.quotation_subjectInternet privacyBasic levelExtended familySocial mediaSociologybusinessmedia_common
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