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Hybride Optimierung fr Dimensionsreduktion
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In der Arbeit von Daniel Lckehe wird ein neues hybrides Verfahren zur Dimensionsreduktion methodisch erarbeitet, analysiert und durch experimentelle Tests mit vorhandenen Methoden verglichen. Hochdimensionale Daten, hufig zusammengefasst unter dem Begriff Big Data, liegen heutzutage in vielen Bereichen vor. Darunter fallen beispielsweise visuell erfasste Informationen, in denen Muster erkannt werden sollen, Anwendungen im medizinischen Bereich sowie Daten aus dem Gebiet der Astronomie. Eine Dimensionsreduktion kann dabei helfen, Informationen aus groen, hochkomplexen Datenstzen zu gewinnen und diese besser verarbeiten zu knnen. So knnen Daten beispielsweise auf einen zweidimensionalen Raum abgebildet und somit fr den Menschen visuell erfassbar werden.
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