Search results for "Probabilistic latent semantic analysis"

showing 3 items of 3 documents

Semantic Computing of Moods Based on Tags in Social Media of Music

2014

Social tags inherent in online music services such as Last.fm provide a rich source of information on musical moods. The abundance of social tags makes this data highly beneficial for developing techniques to manage and retrieve mood information, and enables study of the relationships between music content and mood representations with data substantially larger than that available for conventional emotion research. However, no systematic assessment has been done on the accuracy of social tags and derived semantic models at capturing mood information in music. We propose a novel technique called Affective Circumplex Transformation (ACT) for representing the moods of music tracks in an interp…

FOS: Computer and information sciencesVocabularyComputer scienceMusic information retrievalmedia_common.quotation_subjectSemantic analysis (machine learning)Moodscomputer.software_genreAffect (psychology)SemanticsComputer Science - Information RetrievalSemantic computingMusic information retrievalAffective computingmedia_commonSocial and Information Networks (cs.SI)ta113Probabilistic latent semantic analysisSocial tagsbusiness.industryComputer Science - Social and Information NetworksMultimedia (cs.MM)Semantic analysisComputer Science ApplicationsMoodComputational Theory and MathematicsWeb miningta6131Vector space modelArtificial intelligenceGenresbusinesscomputerComputer Science - MultimediaInformation Retrieval (cs.IR)MusicNatural language processingPrediction.Information SystemsIEEE Transactions on Knowledge and Data Engineering
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Automatic Dictionary Creation by Sub-symbolic Encoding of Words

2006

This paper describes a technique for automatic creation of dictionaries using sub-symbolic representation of words in cross-language context. Semantic relationship among words of two languages is extracted from aligned bilingual text corpora. This feature is obtained applying the Latent Semantic Analysis technique to the matrices representing terms co-occurrences in aligned text fragments. The technique allows to find the “best translation” according to a properly defined geometric distance in an automatically created semantic space. Experiments show an interesting correctness of 95% obtained in the best case.

Text corpusCorrectnessProbabilistic latent semantic analysisComputer scienceLatent semantic analysisbusiness.industryContext (language use)Translation (geometry)computer.software_genreFeature (linguistics)Artificial intelligencebusinessRepresentation (mathematics)computerNatural language processing
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Image classification based on 2D feature motifs

2013

The classification of raw data often involves the problem of selecting the appropriate set of features to represent the input data. In general, various features can be extracted from the input dataset, but only some of them are actually relevant for the classification process. Since relevant features are often unknown in real-world problems, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the candidate feature set. In this paper, we study the capability of a special class of motifs previously introduced in the literature, i.e. 2D irredundant motifs, when they are exploited as feat…

pattern discoveryContextual image classificationProbabilistic latent semantic analysisExploitComputer sciencebusiness.industryScale-invariant feature transformPattern recognitioncomputer.software_genreDigital imageComputingMethodologies_PATTERNRECOGNITIONclassificationimage analysisVisual WordArtificial intelligenceData miningbusinessClassifier (UML)computerImage compression
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